the bits of silence : redundant traffic in voip

The Bits of Silence Redundant Traffic in VoIPMohammad A HoqueUniversity of Helsinki

mohammadahqouehelsinkifi

Petteri NurmiUniversity of Helsinki

petterinurmicshelsinkifi

Matti SiekkinenUniversity of Helsinki

mattisiekkinenhelsinkifi

Pan HuiUniversity of Helsinki HKUST

panhuihelsinkifipanhuicseusthk

Sasu TarkomaUniversity of Helsinki

sasutarkomahelsinkifi

ABSTRACTHuman conversation is characterized by brief pauses and so-calledturn-taking behavior between the speakers In the context of VoIPthis means that there are frequent periods where the microphonecaptures only background noise ndash or even silence whenever the mi-crophone is muted The bits transmitted from such silence periodsintroduce overhead in terms of data usage energy consumption andnetwork infrastructure costs In this paper we contribute by shed-ding light on these costs for VoIP applications We systematicallymeasure the performance of six popular mobile VoIP applicationswith controlled human conversation and acoustic setup Our analy-sis demonstrates that significant savings can indeed be achievablendash with the best performing silence suppression technique beingeffective on 75 of silent pauses in the conversation in a quiet placeThis results in 2-5 times data savings and 50-90 lower energy con-sumption compared to the next better alternative Even then theeffectiveness of silence suppression can be sensitive to the amountof background noise underlying speech codec and the device beingused The codec characteristics and performance do not depend onthe network type However silence suppression makes VoIP trafficnetwork friendly as much as VoLTE traffic Our results provide newinsights into VoIP performance and offer a motivation for furtherenhancements to a wide variety of voice assisted applications assuch intelligent home assistants and other IoT devices

CCS CONCEPTSbullNetworksrarrNetworkperformance evaluation bullComputersystems organizationrarr Embedded systems

KEYWORDSVoIP speech codec turn-taking silence suppression VoLTE IoT

1 INTRODUCTIONVoice over IP (VoIP) has rapidly evolved into the de-facto methodof voice communication across a wide range of devices and plat-forms Examples range from smartphones to speech controlledhomepersonal assistants [4 9 22] and even environment andwildlife monitoring sensors take advantage of VoIP [24 48] Theperformance of VoIP applications is governed by speech codecswhich determine how audio information is encoded and transmit-ted For example WhatsApp and Skype use variants of the Opuscodec [68] whereas Facebook Messenger uses internet Speech Au-dio Codec (iSAC) [36] Similar codecs are also used by wirelessearbuds and headsets [15] personal and home assistants such as

Alexa and Google Home [4 9] While the main task of a speechcodec is to determine how information is encoded and decoded italso determines how transmissions can be adjusted eg by takingadvantage of dynamic transmission rates or switching to a discon-tinuous transmission mode (DTX) Although these measures wereoriginally designed for supporting low bandwidth communicationsthey have been adopted as a mechanism for reducing resource andbandwidth usage with the exponential increase in Internet trafficrecently For example Opus supports DTX during silence

Silence is a fundamental characteristic of speech Indeed hu-man conversations are characterized by brief pauses and so-calledturn-taking behavior whereby people alternate between who iscurrently the active speaker [35 60] In the context of VoIP thismeans that there are frequent periods where the microphone onlycaptures background noise ndash or even silence whenever the micro-phone is muted Unless accounted for this can result in significantprocessing and communication overhead as the correspondingpackets are effectively empty These transmissions also preventthe network interface from taking advantage of power-saving opti-mizations [57] VoIP applications should be able to mitigate thoseeffects of the silent periods by alleviating the transmission of un-necessary information Silence suppression is also essential forminimizing interference in the radio spectrum For example WiFiwireless earbuds cordless phones household appliances rangingfrom microwave ovens to baby monitors and diverse IoT devicesincreasingly all share the unlicensed radio spectrum [59]

Despite the importance of silence suppression surprisingly littleresearch has been carried out on examining how silence periods arehandled by diverse VoIP applications and their impacts on energyand radio resource consumption Indeed previous research hasfocused on developing energy-saving mechanisms that can exploitsilence in VoIP traffic [53 57] and on analysing correspondingnetwork flow properties eg for classifying VoIP traffic [27] forspeaker or dialect identification [43 67 72] or for adapting bufferor smoothing jitter delays [29]

In this paper we contribute by systematically analyzing theperformance characteristics of silence suppression techniques inspeech codecs through five different and popular VoIP applicationson smartphones (Viber [7] Whatsapp [8] Facebook Messenger(Facebook) [5] Skype [6] Duo [3] that each use a different voicecodec As part of our analysis we separately investigate silencesuppression in Voice over LTE (VoLTE) traffic We analyse codecperformance through these leading VoIP applications on smart-phones withwithout wireless earbuds as their implementations ofcodecs are likely to be highly optimized to provide best overall user

Conferencersquo17 July 2017 Washington DC USA Hoque et al

Automotive acoustic diagnosis

EchoHome Assistant

Noise monitoring in smart cities

Edge AI poweredMedical devices

body worn camerababy monitor

EarbudsSmart devicesInternet

VoIP (voice speech coding)

BLE (voice speech

coding)

Figure 1 Common examples of smart devices and applica-tions that utilise speech codecs

experience We carry out our analysis by developing an acous-tic measurement setup which allows controlled evaluation of theeffects of silence suppression on VoIP applications in a range of set-tings We investigate how the different VoIP communication meansadopt silence suppression characterise the mechanisms that theyemploy and quantify traffic energy and LTE resource usage Wecarry out our investigation considering three conversation contextswhere the silence suppression mechanisms are expected to improvethe resource utilization of VoIP applications These contexts rep-resent different points along the background noise spectrum ofeveryday situations (quiet environment and two noisy environ-ments with varying characteristics of noise)

Our results show that codecs in contemporary VoIP applicationshave diverse measures for silence periods with characteristics ofthese measures varying significantly across applications Thesecharacteristics have wide-ranging effects on data savings energyconsumption networking resources and even privacy for variousVoIP applications While we investigate mobile VoIP applicationsour findings are relevant and applicable to the wide range of devicesand applications that utilise voice codecs ndash see Figure 1 for examplesof these kinds of devicesHighlights of Findings and AnalysisSilence Suppression We demonstrate that there is significantvariability in the use of silence suppression techniques across theapplications and their effectiveness is highly variable across callcontexts Facebook and Viber offer the most savings followed byWhatsApp Facebook suppresses 75 of the silences in speech ina quiet room but only 48 effective in a coffee shop or in a livingroom setting with ambient noise For Viber the correspondingsavings are 39 and 30 Consequently Facebook transmits 23times less traffic compared to Viber Although WhatsApp employssilence suppression it does not eliminate traffic and fails to offerany data savings when the context changes Skype offers significantdata savings when silence is suppressed by muting the microphoneResource EffectivenessWe demonstrate that Facebook and Duoare the least and most energy consuming applications respectivelyFacebook is 25-190 more energy efficient for different noise con-texts compared to other applications Skype andWhatsApp perform

similarly though Skype has higher bitrates than WhatsApp Viberalso suppresses traffic for silent periods however it consumes moreenergy than Skype andWhatsApp and the likely reason is the codecDuo consistently consumes more energy in the presence or absenceof noise However a group conversation may have 2-3 time energysavings when most of the participants suppress silence by mutingthe microphonesNetwork Flow Properties Facebook Viber andWhatsApp adapttheir traffic and flow properties according to conversation patternsFacebook is the best alternative for conversations in a quiet placewhereas Viber and WhatsApp are suitable for noisy places consid-ering data savings and energy consumption respectively Subse-quently the VoIP traffic from these applications is network friendlyas much as VoLTE as we demonstrate The usage of Bluetooth lowenergy (BLE) earbuds and changes in network type ie WiFiLTEdo not affect the performance of the applications

2 EXPERIMENTSWe analyze silence suppression noise resiliency energy consump-tion and network friendliness in mobile VoIP through carefullycontrolled benchmarks conducted using a representative set ofapplications and conversation contexts In the following we de-scribe the applications considered in our study acoustic analysisof overall experimental setup and the tools used to collect variousmeasurement data

21 VoIP ApplicationsWe analyse codec performance by considering five popular mobileVoIP applicationsSkype establishes P2P sessions for one-to-one calls It relies on aserver for conferencing calls with multiple participants The net-work consists of two types of nodes ordinary hosts running Skypeand supernodes acting as gateways to the Skype network Any nodewith the public IP address and adequate resources could operateas a supernode At present there are a fixed number of dedicatedsupernodes operated by Skype in the cloud Traffic is transmitted di-rectly over UDP unless one of the devices is behind a port-restrictedNAT and UDP-restricted firewall in which case traffic is routedthrough the super nodes [19] The latest Skype applications useOpus codec for encoding voice [68]Viber uses dedicated and dynamically allocated servers to mediatethe communication between the call participants [49] Similarlyto Skype signaling takes place over TCP The media is encodedwith IP-MR codec [1] and UDP packets are exchanged through thedynamically assigned serversWhatsApp uses similar client-server architecture as Viber Thecall signaling takes place over TCP and the media is transmittedover UDP Similar to Skype it uses Opus [42]Facebook Messenger amp Duo use WebRTC for VoIP call connec-tions WebRTC [36] relies on the applications for P2P call setupand media is exchanged directly between the participants overUDP [55] WebRTC integrates several codecs such as Opus iSACG711 and G722 [2] The Facebook messenger and Duo may useany of these codecs depending on the platform and peer

The Bits of Silence Redundant Traffic in VoIP Conferencersquo17 July 2017 Washington DC USA

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(a) One-to-One conversation Setup

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Figure 2 Acoustic experiment setup for one-to-one (a) and group conversations (b)

We chose these applications as they are the most widely installedapplications on both iOS and Android devices and as they integratedifferent codecs The applications are also highly popular Facebookand WhatsApp messenger reportedly have over one billion activeusers per month [64] whereas Skype and Viber report around 300million active users per month [21 65] For Duo exact statisticsare not openly available but based on downloads we would expectsimilar numbers also

22 Acoustic Experiment SetupWe conduct all experiments with one-to-one conversations station-ary in a 6 m2 room and use different audios to emulate backgroundnoise or contexts as shown in Figure2(a) A MacBookPro manip-ulates the background audio to the desired level in the room asdescribed in the following section A second MacBookPro producesthe speech signal and two smartphones are placed nearby itrsquos speak-ers This setting allows us to measure the effectiveness of silencesuppression techniques on two different devices at the same timeand offers control over noise levels and speech patterns unlike aconventional setup using two speakers in separate rooms We notethat while we use laptop speakers for playing out the conversa-tions this is unlikely to affect the results Human speech frequencyis in the range 85 to 250 depending on the human speakerrsquos agegender and other factors The frequency response of microphonesand speakers is optimized for this range resulting in high fidelityoutput and capture of voice Unnatural sounding artefacts generallyare result of pink noise which mostly affects higher frequencies ofthe audio spectrum As these artefacts reside in different frequen-cies than speech they can be filtered out by the codec and hencethey do not interfere with silence suppression

For group conversations the devices of the caller and the calleewere in separate rooms with similar background noise levels andwithout any additive noise as shown in Figure 2(b) The audiovolume of the devices was set to the maximum during the callsThe speaker of the smartphones was switched off to avoid echosfrom a double conversation effect as the participants are very closeand listening on the same conversation

To obtain further insights into behaviour of silence suppressionmechanisms with different codecs and different types of smartdevices we repeated the one-to-one conversations using two BLEearbuds Airpod 2 and Jabra 65t Elite These earbuds use sub-bandcoding (SBC) for Bluetooth SBC also supports above mentionedcodecs and DTX [15] and negotiates the codec call-by-call basis

23 Conversation amp ContextsWe perform our experiments considering three conversation con-texts that have been designed to emulate common everyday situ-ations and be in line with daily routines [18] The three contextswe consider are (i) noiseless conversations (ii) conversations withmoderate noise and (iii) conversations with extreme noise Thefirst two contexts represent typical indoor contexts and the lastone emulates a noisier outdoor context in line with characteristicsof daily routines(1) Noiseless ConversationsWe first initiate VoIP calls betweentwo devices in a noise-reduced room Devices of both call partici-pants were placed in the same room The average sound level inthe room was 343 dBA which is in line with indoor sound levelsreported in other studies [47] The audio conversation was recordedfrom a high definition YouTube recording of everyday English con-versation1 and replayed on a MacBook Pro after initiating the callsThe conversation has moderate pauses and speech events as shownin Figure 3 (b) We computed the duration of the speech and pauseevents using a moving maximum function in Matlab [31] We usedthe amplitude 001 as the threshold for the moving function to sep-arate the silent periods This threshold value is derived from thetiny spike at 52nd second in Figure 3 (a) The output volume ofthe laptop was 75 of the maximum In Figure 3 (c) we notice thesound level of the speech varies around the average sound levelof the room We conduct two sets of experiments First both thecaller and callee remain silent Second both of them are exposed tothe conversation speech These experiments demonstrate whetherthe applications can recognize the long and brief pauses duringconversations at home or office ie in a noise-free or noise-reducedenvironment(2) Conversations with Noise In urban settings we are exposedto more sound in indoor public places or outdoor For examplein NYC people experience a mean sound level of 756 and 744dBA due to pedestrian and transport traffic [50] The communitygathering in a living room can create an annoying sound of 55dBA [70] We repeat experiments by playing two other YouTubeclips one of a coffee shop2 and one of a kindergarten playground3on top of the conversations The corresponding sound level of theaudios is presented in Figure 3 (c) We played the audios on anotherMacBookPro and measured the sound level on iPhone 6 which wasfrom 15 meters away from the other MacBook Similar to the earliersetup the output volume of both laptops was 75 of the maximumFigure 3 depicts the distributions of sound levels in these audios1httpswwwyoutubecomwatchv=OHK-xsvW0TQ2httpswwwyoutubecomwatchv=BOdLmxy06H03httpswwwyoutubecomwatchv=wAjKpdokhls


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Figure 3 The acoustic properties of the conversation speech and the sound pressure level with different experiment setup (a)The raw speech signal (b) The duration of pauses in the conversations (c) Sound pressure levels for different call contextsmeasured with Decibel [11] Decibel uses on-devicemicrophones andmeasures sound pressure level at 5Hz Themeasurementrange is 30-130 DBA as the mics on smartphones are usually designed for the human voice

Conversation Type Caller CalleeOne-to-One(VoIP) iPhone 6 Nexus 6One-to-One (VoLTE) Xiaomi Mi8 Samsung S9Group (VoIP) iPhone 6 Nexus 6 Mi8 S9Table 1 Conversation Experiments and the Devices used

over time These experiments assess whether the applications canidentify pauses when conversation context becomes noisier(3)Muted ConversationsDuring the turns of an inactive speakerthe applications may transmit ambient noise Muting the micro-phone can suppress such noise and it is prevalent during groupconversation over VoIP We concatenated the same audio signal pre-sented in Figure 3 (a) for 195s to simulate the group conversationWe conduct two sets of such experiments with the VoIP applicationsWe formed application-specific groups of four participants exceptfor Duo which did not support this functionality across the testdevices during the time of the experiments First all participantsactively participate in the conversation and then leave one afteranother in the noise-free setup emulating group dynamics Nextwe experimented by muting the microphone of the devices Threeparticipants mute their microphones one after another

24 Measurement Tools amp ConfigurationsWe experiment with five VoIP applications with different conversa-tion types as shown in Table 1 The one-to-one conversations tookplace in the presence of WiFi and LTE networks We also used BLEheadsets during some calls On the other hand group conversationstook place over LTE without any earbuds Conversation for everycontext was repeated three times for each of the applicationsNetwork Layer Traffic Measurements VoIP traffic is capturedusing tcpdump on a remote virtual interface [14] RVI on iPhone6 from a MacBookPro Traffic is captured using tcpdump on thevirtual interface In total we analyze around 250 VoIP call traces of290 minutes Unlike the VoIP traffic VoLTE traffic does not travelthrough the TCPIP stack of the OS kernel Therefore we could notcapture VoLTE trafficPhysical Layer Resource Measurements We also logged LTEphysical layer resource block (RB) and VoLTE information using

the Network Signal Guru (NSG) [13] NSG only works on rooteddevices with LTE Qualcomm chipset (Nexus 6 Xiaomi Mi8) RB isthe unit of LTE network resource [41]Energy Consumption MeasurementsWe separately measuredthe energy consumption of Nexus 6 during VoIP conversations ac-cording to the contexts Nexus 6 has a Coulomb counter interfacesthat enable on-device current measurement at 6Hz [12] We devel-oped an energy profiler that uses Android APIs to sample the runtime current consumption of Nexus 6 at 2Hz

3 ONE-TO-ONE CONVERSATIONSAccording to human conversation theory an inactive speaker canbe silent while waiting for his turn during conversations whilelistening to the active speaker And the active speaker may haveaverage pauses of 200-600 ms in the speech [35] In this sectionwe demonstrate that only a few modern VoIP applications candetect silence in conversations and suppress traffic during thosesilent periods Indeed their efficiency depends on call contexts orsurrounding noise They are less efficient in a moderately noisyenvironment such as in a coffee shop or a living room compared toa silent place Their performance diminishes as the context becomescomparatively noisier and as the context changes the applicationsemploy different packet sizes and packet gaps

31 Silent Turns and Inactive SpeakersDuring a conversation the active speaker utters phrases subse-quently with brief pauses whereas the inactive speaker listens tothose phrases and waits for hisher turn This waiting time canvary from 600 ms to 2 seconds in natural conversation [44] Wefirst examine how many bytes an application sends to the other endwhen the speaker is inactive or silent ie only background noiseis captured and transported between the devices We use bits persecond to benchmark the application performance

The results in Table 2 show that Facebook exchanges the smallestamount of traffic followed by Viber and WhatsApp These threeapplications would exchange only a hundred bytes for an inactivespeaker during the 1 second of waiting or inactive turn The bi-trates of Skype and Duo are 4-20 times higher than the other threeapplications and they may not suppress silence at all


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Figure 4 The synchronization of speech signal and VoIP traffic of five applications without any additional noise

Device WhatsApp Skype Duo Viber FacebookNexus 6 10 (05) 41 (7) 55 (2) 58 (4) 24 (1)iPhone 6 14 (4) 42 (8) 63 (4) 56 (6) 37 (1)

Table 2 Average bitrates (kbps) of the VoIP applicationsduring the silent turns and standard deviation of measure-ments

During the silent turns both Facebook and Viber flows have thelargest inter-packet gaps whereas WhatsApp packets have mostly60 ms gaps The distributions of packet size in the silent calls alsovary among the applications Viber has smaller bitrates and usessmaller packets (asymp55 bytes) compared to the other applicationsFacebook only sends 2-3 large packets (asymp300 bytes) in a secondwhereas Viber and WhatsApp send 16-20 smaller packets

32 Contexts and Pauses in ConversationsThe previous Section demonstrated that three of the tested VoIPapplications use very low bitrates and two applications apply veryhigh bitrates during the silent turns when the speaker is contin-uously inactive We next examine how the applications performunder natural conversation and how different call contexts affecttheir performance demonstrating that (i) length of pauses is a sig-nificant factor in determining when silence suppression is usedand (ii) amount of noise significantly impacts silence suppressionperformance(1) Noiseless Conversation Figure 4 illustrates bitrates of the ap-plications (bits100ms) WhatsApp maintains a low bitrate duringthe first 30 seconds of the calls but overall its bitrate fluctuates dur-ing the conversation Viberrsquos bitrate also varies similarly Similarlyto WhatsApp and Viber Facebook has fluctuating bitrates whichsuggests that these applications may detect silence and suppressthe packets for those brief pauses in the conversations as well

To understand this effect better we synchronize the bitrateswith the audio signal Figure 4 presents the actual signal and mapsit according to the timestamp of the packets in the traces In thesynchronized signal the conversation begins exactly at the 10th

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Figure 5 The duration of pauses for different call contextsonNexus 6 (Left) and the bitrates of the applications to thosecontext (Right)

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Figure 6 The synchronization of speech signal and bitratesof three applications in the presence of moderate noise

second after the call setup It also demonstrates that the fluctuationsin the bitrates are according to the signal The bitrates of Facebookand Viber drop to zero during the corresponding brief pauses inthe signal After 15th second WhatsApp maintains a minimum


Figure 7 Inter-packet gaps of silent turns and conversations originating from Nexus 6 for different contexts

bitrate higher than zero Nevertheless the pattern from silencesuppression is obvious

For Skype andDuo Figure 4 does not demonstrate similar changesin the bitrates according to the speech signal Rather there are rel-atively small changes in bitrates compared to the silent turns asshown in Figure 5 Duorsquos average bitrate increases by 15 kbps com-pared to the silent turns presented in Table 2 Their performance isin line with their limited suppression performance demonstrated inthe earlier section In Figure 5 the average bitrate is computed fromthe total amount of bytes exchanged during total pause durationfrom three traces for a context

From the presented sampled traces in Figure 5 we compute thetotal duration of pause events for five applications We consider 200ms as the minimum gap in the traces as there is a pause of 200 ms inthe conversation Figure 5 shows that Facebook and Viber on Nexus6 do not send traffic for 212 and 102 seconds respectively duringthe total 265 seconds of brief pauses in the speech Facebook alsoexchanges the smallest amount of traffic followed by Viber andWhatsApp WhatsApp generates mostly 100 ms gaps during thefirst 15 seconds which do not reflect the pauses in the signal duringthat period After that no gaps exist and there is always a smallamount of traffic during those pauses as shown in Figure 4 Thispattern is according to lower bitrates observed during the silentturns presented in Table 2 These results suggest that Facebookmessenger is the most efficient application in suppressing silence ina noise-free conversation However Viber has the smallest bitratefollowed by WhatsApp Facebook Skype and Duo (Figure 5)(2) Conversations with moderate noise We next repeat the ex-periments by adding moderate background noise to the environ-ment as discussed in Section 2 The noise emulates backgroundconversations among multiple people in a coffee shop or in a liv-ing room We play the noisy audio and then establish the call at5th second of noise playback and the conversation speech beginsat the 10th second of the conversation This ensures that all theapplications experience similar noise during conversations

Figure 6 shows that the bitrates of the applications fluctuate evenwith moderate noise except naturally Skype and Duo which do notseem to incorporate silence suppression The figure demonstratesthat Facebook and Viber have moderate gaps according to thespeech signal Figure 5 shows the duration of pauses for applicationswith moderate noise in the room The pause duration decreases andbitrates increase compared to noiseless conversations The savings

from pauses in Facebook and Viber reduce to 48 and 30 of totalpause duration respectively(3) Conversations with extreme noise Finally we compare theapplications under conditions emulating an outdoor context byintroducing extreme noise discussed in Section 2 Figure 5 showsthat only the Facebook conversations had 500 ms pauses accordingto the speech signal The figure also shows that all the applicationshave the highest bitrates in this very noisy call context The bi-trates of WhatsApp Viber and Facebook increases as the contextbecomes noisier As before Skype and Duo have negligible changesin bitrates compared to the less noisy contexts

Compared to the silent turns (Table 2) Facebook Viber andWhatsApp have 2-10 higher bitrates during the speech Skype andDuo have little increase in the bitrates during actual conversation

33 Contexts and Flow PropertiesIn Section 31 we have briefly discussed how the packet gaps andpacket size vary among the applications during the silent turnsThis section examines these properties among applications in lightof the conversation context and the corresponding codec

Figure 7 and 8 show that conversation speech from all the ap-plications experience smaller packet gaps and have larger packetscompared to the silent turns The applications also employ differentflow properties as the conversation context changes from silent tonoisy and noisier as demonstrated in the figures WhatsApp hasalmost a constant inter-packet gap and the packet size increaseswith the ambient noise Viber on the other hand increases packetsize and reduces the packet gap as the intensity of the ambient noiseincreases In the case of Facebook the inter-packet gap increasesand the distribution of packet size remains almost the same Skypeand Duo do not have noticeable variations in the distributions ofpacket gaps and packet size

WhatsApp uses Opus codec [34] which combines SILK andCELT codecs Among these only SILK is a variable bitrate codecFigure 4 shows that the bitrate fluctuates between 10-32 kbps whichsuggests that WhatsApp specifically uses OpusSILK codec Thedistributions of packet gaps in Figure 7 suggest that it encodes 60ms conversation which is also a codec attribute The bitrate alsofluctuates according to the speech signal which in turn emphasizesthat SILK also has built-in support for silence suppression ie DTXHowever the codec does not eliminate traffic completely and thebaseline bitrate is 10 kbps


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Figure 8 Comparison of packet sizes for the silent turns and different conversation contexts on Nexus 6

VoIPApps LTE WiFibitrate(kbps)

pktgap(ms)

pktsize(bytes)

bitrate(kbps)

pktgap(ms)

pktsize(bytes)

Skype 54(12) 20(20) 145(77) 51(15) 20(7) 142(114)WhatsApp 18(6) 69(40) 151(82) 18(7) 70(39) 195(85)Duo 70(10) 17(10) 153(40) 66(12) 17(9) 148(22)Viber 12(6) 71(81) 112(37) 13(6) 68(71) 125(37)Facebook 23(13) 91(150) 243(77) 21(14) 91(135) 245(72)

Table 3 Average flow features of conversations on Nexus 6without any additive noise The numbers inside the paren-theses represent themeasure of spread (standard deviation)

Skype also uses the Opus codec Contrary to WhatsApprsquos perfor-mance we notice almost constant bitrate pattern of Skype in Figure4 and very small packet gaps in Figure 7 Such patterns suggest thatSkype specifically uses OpusCELT which is a low latency constantbitrate codec

On the other hand Viber uses IP-MR codec [1] which also sup-ports variable bitrate Subsequently Figure 4 shows that Viberrsquosbitrate alternates between 12-24 kbps Besides the bitrate constantlyvaries according to pauses in the conversation Therefore this codecalso supports DTX During the pauses the average bitrate can bereduced to 54 kbps

Duo uses WebRTC and thus can use any of the codecs supportedby WebRTC The flow properties ie packet size (160 Bytes) andpacket gaps (17 ms) suggest that Duo uses G711 with constant64 kbps bitrate This codec does not have an integrated silencesuppression technique [76] Consequently the bitrate is alwaysabove 50 kbps as shown in Figure 5 Although Duo does not havenoticeable changes in the packet gaps it uses smaller packets duringthe silent turns

Similar to Duo Facebook Messenger relies on WebRTC frame-work However Facebook uses iSAC codec [33] The bitrate patternsin Figure 4 and 6 suggest that iSAC is a variable bitrate codec Thebitrate pattern fluctuates with the speech signal as it has integratedDTX [51] In the absence of speech the bitrate reduces to 25 kbps

34 Device Variation amp ConnectivityNote that a particular codec may not be tight to a particular appli-cation and vice versa For example Facebook Messenger uses the

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Figure 9 The duration of pauses for different contexts oniPhone 6 (Left) and the bitrates of the applications duringthe pauses according to those context (Right)

Opus codec with Mozilla Firefox browser [33] and the WebRTCprotocol uses G711 as the fallback codec [26]

In Table 2 we have already presented that the applications onthe iPhone have higher bitrates than those on Nexus 6 during thesilent turns Therefore we also computed the total pause dura-tion on iPhone 6 during conversations Figure 9 shows the averagesilent periods from three traces for a context In the figure the aver-age bitrate is computed from the total amount of bytes exchangedduring total pause duration from three traces for a context Thefigure demonstrates that Facebook and Viber suppress traffic alsoon iPhone 6 according to the noise However the silent periodsare smaller and the applications have higher bitrates on iPhone6 compared to Nexus 6 In other words silence suppression effec-tiveness varies across devices and is more efficient on the Androidhandset than on the iPhone in our experiments We could not finda noticeable difference with Nexus 6 flow properties other thanlarger packet sizes

We repeated the experiments with two additional configurationsconversations via BLE earbuds and WiFi We connected a Jabra 65tElite earbud and Apple AirPods 2 with Nexus 6 and iPhone 6 respec-tively These two devices use the SBC having support for the codecsbeing used by the applications on smartphones Nevertheless theapplications have had similar performance in silence suppressionas stated above and the traffic pattern described earlier Althoughan early study found changes in bitrate as the connectivity changesfrom Ethernet to WiMax [32] in our research switching from LTE



1

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Figure 10 The amount of data sent by Nexus 6 during theconversation pauses for different contexts The average iscomputed from three traffic traces for a context

toWiFi did not affect the performance and flow properties as shownin Table 3 It is very likely that applications adapted bitrates as theearlier wireless networks had limited bandwidth

35 SummaryOur results demonstrate that VoIP applications can significantlysave traffic by suppressing silence during the silent turns and pausesin the conversation speech Facebook and Viber can suppress thevery minimum of 200 ms gaps Facebook and Viber can suppresstraffic for 75 and 39 of total silent periods respectively in a silentplace With moderate noise in the room their achievements reduceto 48 and 30 respectively However these two applications offer2-3 times more data savings when conversations take place in a lessnoisy environment followed by WhatsApp Although WhatsAppperforms silence suppression the approach does not eliminatetraffic Skype and Duo do not suppress traffic for silence

G711 (Duo) and OpusCELT (Skype) are the constant bitratecodecs In contrast OpusSILK (WhatsApp) iSAC (Facebook) andIP-MR (Viber) are the variable bitrate codecs The applications sup-press silence with the help of DTX At the same time they react tothe context noise to different degrees Figure 10 demonstrates thatFacebook exchanged 6-7 times more traffic in the noisier or noisiestcontext compared to the noiseless conversation The reaction ofViber to moderate noise is negligible compared to Facebook how-ever the data overhead is twice in the noisiest context SurprisinglyWhatsApp sent almost a similar amount of data irrespective of theconversation context WhatsApp bitrate does not increase eithersignificantly due to additive noise This suggests that OpusSILKdoes not react to noise Figure 10 illustrates similar performance ofSkype and Duo

The performance of the applications suggests that FacebookViber and WhatsApp reduce data waste significantly by detectingsilence and suppressing packets compared to Skype and Duo How-ever their performance can vary according to the device too as wehave compared between Nexus 6 and iPhone 6 Furthermore theaddition of BLE headsets and the selection of WiFi over LTE doesnot affect the selection of codecs or the performance of applicationsIn Section 5 we measure the contribution of silence suppressiontechniques and flow properties on energy consumption and LTEphysical layer resource consumption

WhatsApp Skype Duo Viber Facebook0

20

40

60

avg

b

itra

te (

kb

ps) noiseless silent turns

noiseless muted turns

Figure 11 Bitrates of the applications during the silent andmuted turns on Nexus 6

4 GROUP CONVERSATIONSWith multiple participants in a conversation a user may haveto wait for a longer time for the turn and such silent turns aremore prevalent compared to the one-to-one conversations There-fore muting microphones can be more effective in suppressingsurrounding noise In this section we first study the performanceof applications or codecs for one-to-one conversations by mut-ing the microphone during silent turns After that we investigategroup conversations and how muting the microphone a commonpractice during group calls influences the silent turns We use theconcatenated conversation speech specified in Section 2

41 Muted Turns and Flow PropertiesThe bar chart in Figure 11 shows the bitrates of the applicationsduring the muted turns Facebook has the lowest bitrate followedby Viber Skype WhatsApp and Duo Duorsquos muted stream has5-10 times higher bitrate than the other applications Since themicrophone is mute the applications have nothing to encode andthe corresponding frames are null The applications or the nativeaudio interface ie driver generate these null or muted framesSince different applications have different rates in generating mutedframes on the same device the applications or the correspondingcodec dictates muted frame generation

Muted turns of the applications have similar bitrates (Figure 11)and flow properties to the silent turns from the VoIP applicationsexcept for Skype Skype muted packets have more than 500 msgaps and the maximum is approximately 1 s Facebook also gen-erates muted packets with similar gaps Viber muted packets alsohave longer gaps however they are separated by less than 500 msWhatsApp streams on the other hand have 60 ms gaps Mutingmicrophone does not change the packet gaps of Duo streams What-sApp generates packets of specific 71 bytes whereas Duo packetsare mostly 115 bytes Viber generates tiny packets and most of thepackets are less than 55 bytes

42 Conversations with Muted TurnsIn addition to Nexus 6 and iPhone 6 we used Samsung Galaxy S9and Xiaomi Mi 8 The calls were initiated from the iPhone 6 (caller)and all other devices accepted the calls (callees) All the participantshad outgoing traffic and combined traffic from 3 other participants

(1) Noiseless Group ConversationWefirst examined the flow prop-erties of a quiet VoIP group conversation All participants acceptedthe call within the first 10 seconds of the call All of them used thesame concatenated speech signal The bitrates of the aggregatedstreams are 99 54 37 100 kbps respectively for Skype WhatsApp


30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)

WhatsAppSkypeViberFacebook

First participantleaves conversation

Second participantleaves conversation

Figure 12 Muted group conversation bitrates of the applica-tions observed on iPhone 6 during the common muted pe-riod among the participants

Viber and Facebook The bitrate of an individual participant is onethird of this aggregated bitrate

(2) Muted Turns in Group Conversation The calls were initiatedfrom the iPhone 6 (caller) and all other devices accepted the calls(callees) After the first 30 seconds of the call we muted all thecallee devices for 60 seconds After the mute period had endeddevices dropped the call one at a time every 30 seconds (ie at90 120 and 150 seconds) The iPhone 6 had combined incomingtraffic of 3 muted participants The other devices had a combinedincoming bitrate from 2 muted and a non-muted participant

43 SummaryViber consumes the least data during group conversations followedby WhatsApp and even when the microphone is muted Mutingmicrophone for Skype turns can save 11 times data The aggre-gated bitrates of the muted traffic from 30th to the 140th second ofconversation are illustrated in Figure 12

The figure shows that Skype generates bursty traffic As the num-ber of participants decreases the number of bursts also decreasesThe average bitrate of the grouped muted traffic of Skype is 16 kbpswhich is the sum of bitrates from three participants In the caseof WhatsApp the bitrate is constant 30 kbps which decreases asthe number of participants decreases Similar to Skype the aggre-gated bitrate of WhatsApp is the sum of traffic from three mutedparticipants as shown in Figure 11 Facebook behaves similarly

According to the bitrates of an individual muted turns presentedin Figure 11 Viber should have had an aggregated bitrate of 15kbps Interestingly Viberrsquos group bitrate is even smaller than themuted turn of a single participant With additional muted groupcalls we have found that an individual participant always has 45kbps bitrate and such packets are dropped by the Viber server

Surprisingly all the applications or the corresponding codecsgenerate traffic even when the microphone is muted We can thinkof two reasons behind such muted packets First if there is noconversation data the application generates muted frames andsends to avoid the re-initialization latency of the audio chipset atthe other end hardware [30] Second the muted packets may actas the remedy for NAT port binding issue for UDP flows as thosepackets do not allow to expire the NAT port binding timer [19]

5 RESOURCE CONSUMPTIONIn the earlier sections we have demonstrated how various applica-tions or codecs deal with silent turns pauses in conversation andnoise contexts In this section we investigate the contribution ofsilence suppression and reciprocal contexts on LTE resource con-sumption and the energy consumption of mobile devices It mightalso be interesting to compare all these applications with VoLTEas the resource and energy consumption of VoLTE might be moreconservative compared to these applications

51 LTE Resource Block AllocationNSG takes a snapshot of the LTE network status after every 500ms and generates binary logs We manually extracted the modula-tion schemes and the number of physical resource blocks for bothuplink and downlink during the muted turns and extreme-noisecontext Although it is not possible to characterize these physicallayer parameters for every packet using NSG our findings provideinsights into the network friendliness of the VoIP applications

0 5 10 15 resource blocks

0

02

04

06

08

1extreme-noise conversation

Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)

Figure 13 LTE downlinkResource Block (RB) allocation andModulation Schemes for VoIP conversations on Nexus 6

(1) VoIP Conversations Figure 13 depicts that most of the down-link VoIP traffic is allocated to 4 RBs during both muted turn and theextreme noise call contexts The size of the muted packets does nothave any impact on the RB allocation However the bitrate plays arole in the selection of the modulation scheme We notice that mostof the Skype packets were received using the QPKS modulationscheme during the muted turns Likewise QPSK was applied forFacebook during the muted turns This can be explained with thelarger inter-packet gaps of Skype and Facebook 16QAM is appliedduring the muted turns from WhatsApp and Viber

In the case of extreme noise 64QAM is applied for all the appli-cations traffic except Viber We believe that Viberrsquos smaller packetsize in Figure 8 contributes to selecting 16QAM Facebook trafficwith larger packets has higher RBs allocation as shown in Figure13 Group conversations required mostly 12-28 RBs with 16QAMfor all the applications When the participants were muted Viberrequired 4 RBs with 16QAM

(2) VoLTE Conversations In this case we used NSG on a rootedXiaomi Mi8 device Figure 14 shows that VoLTE conversation uses


(a)

(b)

Figure 14 VoLTE codec and bitrates reported byNSG (a) mutedsilent turns (b) conversations withnoiselessextreme-noise contexts

skype whatsapp viber duo facebook0

100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)


noiseless silent turns



Figure 15 Current drawn by the applications on Nexus 6 forsending voice to and receiving muted traffic from iPhone 6

AMR-WB (AMR-WB) codec [10] When the microphone is mutedthe bitrate is 76 kbps Otherwise the bitrate fluctuates within themaximum of 185 kbps This suggests that this codec suppressessilence [76] During the silent turns QPSK was used and mostly 3RBs were allocated for the packets whereas 16QAM was used and4 RBs were allocated for speech traffic

All the uplink packets were encoded with 16QAM and only1-2 RBs were allocated irrespective of the context and applicationNevertheless such resource consumption of VoLTE traffic is similarto the other VoIP applications except the Facebook Messenger

52 Energy ConsumptionWe characterize the consequence of the context of both sides ofthe conversation on energy consumption We conducted three setsof follow up experiments (i) We muted iPhone 6 to measure theenergy impact of outgoing contextual conversations from Nexus 6(Figure 15) (ii) We muted Nexus 6 to measure the energy impactof incoming contextual iPhone 6 streams (Figure 16) The displayand phone speaker of Nexus 6 were off during the measurements(iii) We measure the energy consumption of Nexus 6 for groupconversations where Nexus 6 receives aggregated conversationand muted traffic from the participants and transmits noiselessconversation We developed an energy measurement tool for Nexus6 to read the battery information at 2 Hz(1) One-to-One conversation (Transmitting Noise) Figure 15demonstrates the Facebook and Duo are the least and most energy-consuming applications respectively Facebook is 25-50 77-13076-140 and 115-190 more energy-efficient than Skype What-sApp Viber and Duo respectively Skype and WhatsApp performsimilarly though Skype has a higher bitrate than WhatsApp in allthe contexts except themuted stream Interestingly Viber consumesmore energy than Skype and WhatsApp even though it suppresses

Skype WhatsApp Viber Duo Facebook0

100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)

nosieless muted turns




Figure 16 Current drawn by the applications on Nexus 6 forreceiving voice from and sendingmuted packets to iPhone6

Application noiseless(mA)

muted turns(mA)

Skype 941 386WhatsApp 615 361Viber 561 331Facebook 280 351

Table 4 Avg current consumption of Nexus 6 during groupconversations During the muted group calls 3 participantsmuted their microphones

traffic for the silent periods as we demonstrated in Section 32This behavior was persistent across multiple measurements andthe codec is the likely reason In general the applications consumemore energy as the context becomes noisier(2) One-to-One conversation (Receiving Noise) In Section 33we have already demonstrated that conversation traffic from iPhonehas higher bitrates than the Nexus 6 Likewise Figure 16 demon-strates higher energy consumption compared to those presentedin Figure 15 However the energy consumption pattern is similarFacebook consumes the least energy and outperforms other appli-cations by similar margins WhatsApp and Viber consume similarenergy as the silence suppression in WhatsApp does not eliminatetraffic completely(3) Group ConversationWe also measured energy consumptionduring two sets of the group calls as presented in Table 4 First allfour participants have conversations without any noise Secondonly one participant speaks and others listen to having their micro-phone muted Table 4 shows that Skype is the most and Facebookis the least energy-consuming application for noiseless group con-versations Muting microphones during group conversations canreduce the energy consumption of applications by 2-3 times

53 SummaryFigure 13 emphasizes the selection of codec by the applications andthe resulting flow properties contribute to LTE RB allocation AMR-WB and IP-MR codecs (Viber) produce small packets which result inonly 4 RBs allocation for transmitting conversation packets VoLTEalso reacts to silence generates small packets contributes to similarRB allocation and selection of modulation schemes In contrast thelarger packets from iSAC contribute to a higher number of RBsallocation for Facebook A side-benefit from silence suppression isthat it can facilitate more effective network resource management


Codec-Application Data Noise Energy NetworkOpusSILK(WhatsApp) yes - yes yesOpusCELT (Skype) no ndash yes noiSAC (Facebook) yes no yes yesIP-MR (Viber) yes no no yesG711 (Duo) no ndash no noAMR-WB (VoLTE) yes ndash yes yes

Table 5 Efficiency features of the identified applications andspeech codecs

by exploiting inter-packet gaps For example Skypersquos average inter-packet gap is 20 ms (Table 3) which means that the allocated RBscan be shared with 20 Skype calls one after another Viber has largeand varying packet gaps and the network can share the RBs withmore calls Speech packets from iSAC require more RBs than theothers however these RBs can be shared with more VoIPVoLTEcalls and less network friendly than IP-MR OpusSILK or AMR-WB In contrast Duo and Skype have smaller packet gaps andthe network can share resources among limited calls compared tothe other codecs Interested readers can follow this tutorial4 onestimating cell capacity for VoLTE calls

The energy measurements suggest that Facebook Messenger(iSAC) is the most energy-efficient VoIP application followed bySkype (OpusCELT) WhatsApp (OpusSILK) Viber (IP-MR) andDuo (G711) These results also highlight the energy efficiency ofthe codecs ie iSAC is the most efficient among these five codecsUnlike data savings context does not contribute to significant en-ergy savings for a particular application or the corresponding codecDuring one-to-one conversations a particular application does nothave significant energy savings by muting microphones either Nev-ertheless the mutingmicrophone can save significant energy acrossall the applications for group conversations

The performance of the VoIP traffic in RB and energy consump-tion suggests that the conversation traffic from these applicationsalso can be served similarly to VoLTE traffic The VoIP traffic canbenefit from the dedicated channel and other optimizations suchas header compression [28 52]

6 DISCUSSIONFrom the analysis in this work the performance of the applicationsor codecs can attribute to four different high-level metrics Theattributes are data efficiency due to silence suppression resiliencyagainst noise energy efficiency and network friendliness as shownin Table 5DataEnergy Consumption Table 5 shows that silence suppress-ing codecs enable data and energy savings for mobile VoIP appli-cations Silence suppression is also vital for Bluetooth earbuds orheadsets as they are battery-powered and the energy is beingspent both by headsets and mobile devices Beyond VoIP silencesuppression is essential for many sensor networking applicationsFor example environment and wildlife monitoring sensors arebattery-powered and always onNetwork Friendliness Periods of heavy network demand whetherdue to holidays disasters or other events are challenging to net-work capacity due to continuous peak demand of network resources4httpwwwtechplayoncom2286-2

Our results showed that use of silence suppression can save 2-5times data depending on the application and the level of backgroundnoise This suggests that silence suppression could help to reducenetwork bandwidth use at peak demand (eg in LTE networksthis results in lower consumption of resource blocks) Our findingsalso suggest that silence suppressing codecs would improve theperformance of cross-technology devices eg BLE headsets ear-buds and other devices operating on the shared unlicensed radiospectrum [56]Privacy While the gain in data waste and energy consumptionare significant due to silence suppression this conversely can harmprivacy The focus of our work is not on exploring the range ofsuch privacy violations indeed several prior studies have shownthat traffic pattern can be exploited to identify the speaker [43] anddialect [69 71 72] Instead our aim has been to highlight charac-teristics of network patterns and demonstrate how these correlatewith speech activity The analysis in Section 3 demonstrated thatFacebook WhatsApp and Viberrsquos packet size distributions are af-fected by the contexts In contrast constant bitrate traffic is resilientagainst such attacks [67] Traffic shaping and padding [17] or traf-fic morphing [73] towards constant bitrate traffic can be used tomitigate privacy vulnerabilities at the expense of higher trafficPerformance and Context-Aware Codec SelectionAt presentinteroperability across devices and platforms play roles in selectingthe appropriate codecs Our findings in Table 5 suggest that voiceprocessing applications can benefit by negotiating codec accordingto desired features eg noise robustness energy consumption orprivacy protection In Section 35 we demonstrated that changingnetwork connectivity to WiFi does not change the application per-formance Nevertheless the applications can negotiate constant bitrate codecs when connected to public WiFi networks for privacy-preserving communication Always on devices like Alexa or GoogleHome also rely on the Opus codec for speech processing [4 9] Sincethese devices stay mostly indoor silence suppressing codecs couldprovide significant data savings However they are always con-nected to power and mostly connected toWiFi networks Thereforeprivacy is an essential concern and such devices can use constantbit rate OpusCELT codec when connected to WiFi On the otherhand earbuds or other battery-powered devices can use iSAC Sim-ilarly an outdoor noise sensing device with microphone [48] canunderstand the noise intensity by analyzing the bitrate from iSACas it reacts to noise more aggressively

7 RELATEDWORKAnalysis of VoIP Applications Baset et al [19] studied key func-tionalities of Skype in terms of login NAT traversal and call estab-lishment Skype has a P2P network architecture with super nodesused to manage user logins Ordinary hosts are the applicationsto place the calls and send messages These hosts rely on variousSTUN protocols to find the NAT and firewalls WhatsApp [42] ap-plication has a client-server architecture Identifying Skype traffichas been an active research area Bonfiglio et al [20] proposed twomethods to identify Skype voice call traffic from a collection of var-ious packet types They first looked into the statistical propertiesof message content and then matched with the Skype voice trafficsources by using Naive Bayesian techniques


VoIP Flow Properties Baset et al [19] investigated Skype packetsize and bitrates as well In addition to packet size Do and Branchused inter-packet gaps to classify VoIP traffic real time [27] In ourstudy we have shown that different applications have differentflow features and such features may also vary according to thecontext A recent study has shows how the traffic capturing toolsmay affect the measurement of these flow properties [38] Alsothe privacy implications of VoIP traffic pattern have received someattention [43 67 72] Wu et al [74] assumed that VoIP applicationssuppress silence Instead of relying on the inter-packet gap or packetsize they proposed a machine learning algorithm to detect voiceactivity in the flow As we have shown in the presence of intensenoise the suppression may not be effective for some applicationsand the proposed method may not work Suh et al [66] detectedVoIP traffic from a Skype Relay node using Skype specific heuristicas the relayed VoIP traffic from different clients form burstsVoIP Performance Most previous works on VoIP performancehave focused on metrics affecting the end-user For example Chenet al [23] quantified the impact of bitrate jitter and loss and delayon QoE They further investigated the playout buffer managementalgorithms of three VoIP applications in [75] Using this approachthe authors identified the inefficiency of the buffer managementalgorithm of the applications and suggested to use a modified algo-rithm to maintain optimal user satisfaction Andersson et al [16]studied the impact of various VoIP codecs on QoE in the LTE net-works through simulation In this article we have investigatedthe flow features for different applications and found that a com-bination of smaller packet sizes and the higher inter-packet gapmay influence the QoE negatively VoIP applications also may setDSCP IP flags for different QoS guarantees from the network [37]Recently Skype performance was studied in a high-speed train con-text [45] Dasari et al measured the energy consumption of Skypevideo calls with different mobile devices [25] Finally Raumlmo andToukomaa characterized the voice quality of various speechvoicecodecs with subjective tests [58]Overhead in Wireless Communication Traffic overhead withthe encrypted traffic comes from the TLS handshake and there isadditional energy cost due to encryption Naylor et al [54] investi-gated the performance of HTTPS from traffic traces The potentialof data waste with multimedia streaming applications is quite highdue to unnecessary content download [40 46] Similarly to thesilence packets TCP-based multimedia applications also exchangeonly protocol messages in the case of active flow control triggeredby the streaming applications [39] Sieber et al [62 63] quantifiedYouTubersquos traffic pattern with static and dynamic network condi-tions and found that YouTube may download 33 redundant traf-fic under dynamic network conditions Bartendr [61] and eSched-ule [40] also optimize energy consumption by pre-fetching contentHowever these energy-saving approaches do not apply to the VoIPapplications as the traffic is real time and bi-directional

8 CONCLUSIONSMotivated by the increased and diverse usage of voice over IP ap-plications this measurement study reveals the data and energycost offered by the silent suppression techniques of the underlyingcodecs An efficient codec with silence suppression can be 50-200

more energy efficient compared to the other alternatives Howeversuch effectiveness is sensitive to the background noise The perfor-mance also vary across handsets or operating systems Interestinglysilence suppression makes VoIP traffic network friendly as much asVoLTE traffic Conversely our results also highlighted that silencesuppression might reveal conversational patterns through networkflow properties Therefore VoIP applications or devices can benefitsignificantly by selecting codecs based on the conversation con-text data savings energy consumption and privacy requirementsFinally our acoustic measurement setup provides a reproducible en-vironment for testing debugging and evaluating different speechcodecs The use of silence and muted conversations provides abound for the true performance of the codec whereas recordedspeech with added background noise allows investigating differentconversation contexts

ACKNOWLEDGEMENTSWe thank our shepherd Dr Emir Halepovic and the anonymousreviewers for the valuable feedback which helped to improve thepaper The research is supported by the Academy of Finland projectsgrant no 1319017 319017 5GEAR FIT project and project 16214817from the Research Grants Council of Hong Kong

REFERENCES[1] Viber Turns to SPIRIT for High Quality HD Mo-

bile VoIP Calling httpswwwspiritdspcomnews140-viber-turns-to-spirit-for-high-quality-hd-mobile-voip-calling 2011Last Accessed 11122019

[2] WebRTC - Frequent Questions httpswebrtcorgfaq 2011 Last Accessed12072019

[3] Duo httpsplaygooglecomstoreappsdetailsid=comgoogleandroidappstachyon 2018

[4] Google Nest and Google Home device specifications httpssupportgooglecomgooglenestanswer7072284hl=en 2018 Last Accessed 28082019

[5] Messenger httpsplaygooglecomstoreappsdetailsid=comfacebookorca2018

[6] Skype httpsplaygooglecomstoreappsdetailsid=comskyperaider 2018[7] Viber httpsplaygooglecomstoreappsdetailsid=comvibervoip 2018[8] WhatsApp httpsplaygooglecomstoreappsdetailsid=comwhatsapp 2018[9] Alexa Is Listening All The Time Here is How To Stop It httpswwwforbescom

sitestjmccue20190419alexa-is-listening-all-the-time-heres-how-to-stop-it2019 Last Accessed 3082019

[10] AMR-WBG7222 httpwwwvoiceagecomAMR-WBG7222html 2019 LastAccessed 13102019

[11] Decibel X dB dBA Noise Meter httpsappsapplecomusappdecibel-x-db-dba-noise-meterid448155923 2019

[12] Measuring Device Power httpssourceandroidcomdevicestechpowerdevice2019

[13] Network Signal Guru httpsplaygooglecomstoreappsdetailsid=comqtrunQuickTest 2019

[14] Recording a Packet Trace httpsdeveloperapplecomdocumentationnetworkrecording_a_packet_trace 2019 Last Accessed 23122019

[15] SBC Audio Codecs Supported (Transcoding and Pass-Through) httpssupportsonusnetdisplaySBXDOC61Audio+Codecs 2019 Last Accessed 31102019

[16] K Andersson S A M Mostafa and R Ui-Islam Mobile VoIP user experience inLTE year=2011 In 2011 IEEE 36th Conference on Local Computer Networks pages785ndash788 Oct

[17] N Apthorpe D Reisman S Sundaresan A Narayanan and N Feamster Spyingon the smart home Privacy attacks and defenses on encrypted iot traffic ArXivabs170805044 2017

[18] N Banovic T Buzali F Chevalier J Mankoff and A K Dey Modeling and Un-derstanding Human Routine Behavior In Proceedings of the 2016 CHI Conferenceon Human Factors in Computing Systems CHI rsquo16 pages 248ndash260 New York NYUSA 2016 ACM

[19] S Baset and H Schulzrinne An Analysis of the Skype Peer-to-Peer InternetTelephony Protocol In INFOCOM IEEE 2006

[20] D Bonfiglio M Mellia M Meo D Rossi and P Tofanelli Revealing SkypeTraffic When Randomness Plays with You SIGCOMM Comput Commun Rev


37(4)37ndash48 Aug 2007[21] R Browne Microsoftrsquos Skype gets a redesign ditching Snapchat-

like feature lsquoHighlightsrsquo httpswwwcnbccom20180903microsoft-owned-skype-redesign-ditches-snapchat-like-highlightshtml2018 Last Accessed 26032020

[22] L Caviglione A first look at traffic patterns of Siri Transactions on EmergingTelecommunications Technologies 26nandashna 08 2013

[23] K-T Chen C-Y Huang P Huang and C-L Lei Quantifying Skype User Sat-isfaction In Proceedings of the 2006 Conference on Applications TechnologiesArchitectures and Protocols for Computer Communications SIGCOMM rsquo06 pages399ndash410 New York NY USA 2006 ACM

[24] K Darras P Puumltz Fahrurrozi K Rembold and T Tscharntke Measuring sounddetection spaces for acoustic animal sampling and monitoring Biological Conser-vation 20129 ndash 37 2016

[25] M Dasari S Vargas A Bhattacharya A Balasubramanian S R Das and M Fer-dman Impact of Device Performance on Mobile Internet QoE In Proceedingsof the Internet Measurement Conference 2018 IMC rsquo18 pages 1ndash7 New York NYUSA 2018 ACM

[26] M Developers Codecs used by webrtc httpsdevelopermozillaorgen-USdocsWebMediaFormatsWebRTC_codecs 2019 Last Accessed 27082019

[27] L H Do and P Branch Real Time VoIP Traffic Classification Technical reportSwinburne University of Technology Melbourne Australia 2009

[28] A Elnashar M A El-Saidny and M Yehia Performance evaluation of voltebased on field measurement data ArXiv abs181002968 2018

[29] D Florencio and L He Enhanced adaptive playout scheduling and loss conceal-ment techniques for voice over ip networks In 2011 IEEE International Symposiumof Circuits and Systems (ISCAS) pages 129ndash132 May 2011

[30] G Gokul Y Yan K Dantu S Y Ko and L Ziarek Real Time Sound Processing onAndroid In Proceedings of the 14th International Workshop on Java Technologiesfor Real-Time and Embedded Systems JTRES rsquo16 pages 31ndash310 New York NYUSA 2016 ACM

[31] A Grinsted Moving averages Moving median etc httpswwwmathworkscommatlabcentralfileexchange8251-moving-averages-moving-median-etcRetrieved January 1 2020

[32] E Halepovic M Ghaderi and C Williamson Multimedia application perfor-mance on a wimax network Proceedings of SPIE - The International Society forOptical Engineering 7253 01 2009

[33] P Hancke Messenger exposed Investigative report httpswebrtchackscomwp-contentuploads201505messenger-reportpdf pages 01ndash15 2015 Last Ac-cessed 12112019

[34] P Hancke Whatsapp exposed Investigative report httpswebrtchackscomwp-contentuploads201504WhatsappReportpdf pages 01ndash16 2015 Last Ac-cessed 13102019

[35] M Heldner and J Edlund Pauses gaps and overlaps in conversations J Phonetics38555ndash568 2010

[36] C Holmberg S Hakansson and G Eriksson Web real-time communication usecases and requirements Request for Comments (RFC) 7478 2015

[37] M A Hoque H Abbas T Li Y Li P Hui and S Tarkoma Barriers in seamlessqos for mobile applications 2018 arXiv180900659

[38] M A Hoque A Rao and S Tarkoma In situ network and applicationperformance measurement on android devices and the imperfections 2020arXiv200305208

[39] M A Hoque M Siekkinen and J K Nurminen TCP Receive Buffer AwareWireless Multimedia Streaming An Energy Efficient Approach In Proceeding ofthe 23rd ACM Workshop on Network and Operating Systems Support for DigitalAudio and Video NOSSDAV rsquo13 pages 13ndash18 New York NY USA 2013 ACM

[40] M A Hoque M Siekkinen and J K Nurminen Using crowd-sourced viewingstatistics to save energy in wireless video streaming In Proceedings of the 19thAnnual International Conference on Mobile Computing and Networking MobiComrsquo13 pages 377ndash388 New York NY USA 2013 ACM

[41] J Huang F Qian A Gerber Z M Mao S Sen and O Spatscheck A closeexamination of performance and power characteristics of 4G LTE networks InProceedings of the 10th international conference on Mobile systems applicationsand services MobiSys rsquo12 pages 225ndash238 New York NY USA 2012 ACM

[42] F Karpisek I Baggili and F Breitinger WhatsApp network forensics Decryptingand understanding the WhatsApp call signaling messages Digital Investigation15110 ndash 118 2015 Special Issue Big Data and Intelligent Data Analysis

[43] L Khan M Baig and A M Youssef Speaker recognition from encrypted voipcommunications Digital Investigation 7(1)65 ndash 73 2010

[44] S C Levinson and F Torreira Timing in turn-taking and its implications forprocessing models of language In Front Psychol 2015

[45] L Li K Xu D Wang C Peng K Zheng H Wang R Mijumbi and XiangxiangWang A measurement study on Skype voice and video calls in LTE networks onhigh speed rails In 2017 IEEEACM 25th International Symposium on Quality ofService (IWQoS) pages 1ndash10 June 2017

[46] X Li M Dong Z Ma and F Fernandes GreenTube Power Optimization forMobile Video Streaming via Dynamic Cache Management In Proceedings of theACM Multimedia acmmmrsquo12 New York NY USA 2012 ACM

[47] B Locher A Piquerez M Habermacher M S Ragettli M Roumloumlsli M Brink C Ca-jochen D Vienneau M A Foraster U Muumlller and J M Wunderli Differencesbetween outdoor and indoor sound levels for open tilted and closed windowsIn International journal of environmental research and public health 2018

[48] P Maijala Z Shuyang T Heittola and T Virtanen Environmental noise mon-itoring using source classification in sensors Applied Acoustics 129258 ndash 2672018

[49] R Marik P Bezpalec J Kucerak and L Kencl Revealing viber communicationpatterns to assess protocol vulnerability In 2015 International Conference onComputing and Network Communications (CoCoNet) pages 496ndash504 Dec 2015

[50] T P McAlexander R R M Gershon and R L Neitzel Street-level noise in an ur-ban setting assessment and contribution to personal exposure In EnvironmentalHealth 2015

[51] M Menth A Binzenhoumlfer and S Muumlhleck Source models for speech trafficrevisited IEEEACM Trans Netw 17(4)1042ndash1051 Aug 2009

[52] M Mohseni S Banani A Eckford and R Adve Scheduling for volte Resourceallocation optimization and low-complexity algorithms IEEE Transactions onWireless Communications 181534 ndash 1547 01 2019

[53] V Namboodiri and L Gao Towards energy efficient voip over wireless lans InProceedings of the 9th ACM international symposium on Mobile ad hoc networkingand computing pages 169ndash178 ACM 2008

[54] D Naylor A Finamore I Leontiadis Y Grunenberger M Mellia M MunafograveK Papagiannaki and P Steenkiste The cost of the s in https In Proceedings ofthe 10th ACM International on Conference on Emerging Networking Experimentsand Technologies CoNEXT rsquo14 pages 133ndash140 New York NY USA 2014 ACM

[55] N Pathania R Singh isha and A Malik Comparative Study of Audio and VideoChat Application Over the Internet In 2018 International Conference on IntelligentCircuits and Systems (ICICS) pages 251ndash257 April 2018

[56] T Pulkkinen J Nurminen and P Nurmi Understanding WiFi Cross-TechnologyInterference Detection in the Real World In Proceedings of the 40th InternationalConference on Distributed Computing Systems (ICDCS) IEEE 2020

[57] A J Pyles Z Ren G Zhou and X Liu Sifi exploiting VoIP silence for WiFienergy savings in smart phones In Proceedings of the 13th international conferenceon Ubiquitous computing pages 325ndash334 ACM 2011

[58] A Raumlmouml and H Toukomaa Voice quality characterization of ietf opus codec InINTERSPEECH 2011

[59] S Rayanchu A Patro and S Banerjee Airshark Detecting non-wifi rf devicesusing commodity wifi hardware In Proceedings of the 2011 ACM SIGCOMMConference on Internet Measurement Conference IMC rsquo11 page 137ndash154 NewYork NY USA 2011 Association for Computing Machinery

[60] H Sacks E A Schegloff and G Jefferson A simplest systematics for the or-ganization of turn taking for conversation In Studies in the organization ofconversational interaction pages 7ndash55 Elsevier 1978

[61] A Schulman V Navda R Ramjee N Spring P Deshpande C Grunewald K Jainand V N Padmanabhan Bartendr a practical approach to energy-aware cellulardata scheduling In Proceedings of the sixteenth annual international conferenceon Mobile computing and networking MobiCom rsquo10 pages 85ndash96 New York NYUSA 2010 ACM

[62] C Sieber A Blenk M Hinteregger and W Kellerer The cost of aggressive httpadaptive streaming Quantifying youtubersquos redundant traffic In 2015 IFIPIEEEInternational Symposium on Integrated Network Management (IM) pages 1261ndash1267 May 2015

[63] C Sieber P Heegaard T Hoszligfeld and W Kellerer Sacrificing efficiency for qual-ity of experience YouTubersquos redundant traffic behavior In 2016 IFIP NetworkingConference (IFIP Networking) and Workshops pages 503ndash511 May 2016

[64] Statistia Most popular global mobile messenger apps as of October 2019 basedon number of monthly active users httpswwwstatistacomstatistics258749most-popular-global-mobile-messenger-apps 2019 Last Accessed 25032020

[65] F Statistics Mind-Blowing Viber Statistics https99firmscomblogviber-statistics 2020 Last Accessed 26032020

[66] K Suh D R Figueiredo J Kurose and D Towsley Characterizing and DetectingSkype-Relayed Traffic pages 1ndash12 April 2006

[67] T Vaidya T Walsh and M Sherr Whisper A unilateral defense against voiptraffic re-identification attacks In Proceedings of the 35th Annual ComputerSecurity Applications Conference ACSAC rsquo19 pages 286ndash296 New York NY USA2019 ACM

[68] J Valin K Vos T Terriberry and A Moizard RFC 6716 Definition of the Opusaudio codec Internet engineering task force (IETF) standard 2012

[69] A MWhite A R Matthews K Z Snow and F Monrose Phonotactic reconstruc-tion of encrypted voip conversations Hookt on fon-iks In 2011 IEEE Symposiumon Security and Privacy pages 3ndash18 May 2011

[70] WHO Common noise guideline values httpswwwwhointdocstorepehnoiseComnoise-4pdf pages 55ndash65 2013 Last Accessed 10102019

[71] C V Wright L Ballard S E Coull F Monrose and G M Masson Spot me ifyou can Uncovering spoken phrases in encrypted voip conversations In 2008IEEE Symposium on Security and Privacy (sp 2008) pages 35ndash49 May 2008

[72] C V Wright L Ballard F Monrose and G M Masson Language identificationof encrypted voip traffic Alejandra y roberto or alice and bob In Proceedings of


16th USENIX Security Symposium on USENIX Security Symposium SSrsquo07 pages41ndash412 Berkeley CA USA 2007 USENIX Association

[73] C V Wright S E Coull and F Monrose Traffic morphing An efficient defenseagainst statistical traffic analysis In In Proceedings of the 16th Network andDistributed Security Symposium pages 237ndash250 IEEE 2009

[74] C-C Wu K-T Chen Y-C Chang and C-L Lei Detecting VoIP Traffic Basedon Human Conversation Patterns In H Schulzrinne R State and S Niccolinieditors Principles Systems and Applications of IP Telecommunications Servicesand Security for Next Generation Networks pages 280ndash295 Berlin Heidelberg

2008 Springer Berlin Heidelberg[75] C-C Wu K-T Chen C-Y Huang and C-L Lei An Empirical Evaluation of VoIP

Playout Buffer Dimensioning in Skype Google Talk and MSN Messenger InProceedings of the 18th International Workshop on Network and Operating SystemsSupport for Digital Audio and Video NOSSDAV rsquo09 pages 97ndash102 New York NYUSA 2009 ACM

[76] R Zopf RFC 3389 Real-time Transport Protocol (RTP) Payload for ComfortNoise (CN) Internet engineering task force (IETF) standard 2002

Abstract

1 Introduction

2 Experiments

21 VoIP Applications

22 Acoustic Experiment Setup

23 Conversation amp Contexts

24 Measurement Tools amp Configurations

3 One-to-One Conversations

31 Silent Turns and Inactive Speakers

32 Contexts and Pauses in Conversations

33 Contexts and Flow Properties

34 Device Variation amp Connectivity

35 Summary

4 Group Conversations

41 Muted Turns and Flow Properties

42 Conversations with Muted Turns

43 Summary

5 Resource Consumption

51 LTE Resource Block Allocation

52 Energy Consumption

53 Summary

6 Discussion

7 Related Work

8 Conclusions

References


Automotive acoustic diagnosis

EchoHome Assistant

Noise monitoring in smart cities

Edge AI poweredMedical devices

body worn camerababy monitor

EarbudsSmart devicesInternet

VoIP (voice speech coding)

BLE (voice speech

coding)

Figure 1 Common examples of smart devices and applica-tions that utilise speech codecs

experience We carry out our analysis by developing an acous-tic measurement setup which allows controlled evaluation of theeffects of silence suppression on VoIP applications in a range of set-tings We investigate how the different VoIP communication meansadopt silence suppression characterise the mechanisms that theyemploy and quantify traffic energy and LTE resource usage Wecarry out our investigation considering three conversation contextswhere the silence suppression mechanisms are expected to improvethe resource utilization of VoIP applications These contexts rep-resent different points along the background noise spectrum ofeveryday situations (quiet environment and two noisy environ-ments with varying characteristics of noise)

Our results show that codecs in contemporary VoIP applicationshave diverse measures for silence periods with characteristics ofthese measures varying significantly across applications Thesecharacteristics have wide-ranging effects on data savings energyconsumption networking resources and even privacy for variousVoIP applications While we investigate mobile VoIP applicationsour findings are relevant and applicable to the wide range of devicesand applications that utilise voice codecs ndash see Figure 1 for examplesof these kinds of devicesHighlights of Findings and AnalysisSilence Suppression We demonstrate that there is significantvariability in the use of silence suppression techniques across theapplications and their effectiveness is highly variable across callcontexts Facebook and Viber offer the most savings followed byWhatsApp Facebook suppresses 75 of the silences in speech ina quiet room but only 48 effective in a coffee shop or in a livingroom setting with ambient noise For Viber the correspondingsavings are 39 and 30 Consequently Facebook transmits 23times less traffic compared to Viber Although WhatsApp employssilence suppression it does not eliminate traffic and fails to offerany data savings when the context changes Skype offers significantdata savings when silence is suppressed by muting the microphoneResource EffectivenessWe demonstrate that Facebook and Duoare the least and most energy consuming applications respectivelyFacebook is 25-190 more energy efficient for different noise con-texts compared to other applications Skype andWhatsApp perform

similarly though Skype has higher bitrates than WhatsApp Viberalso suppresses traffic for silent periods however it consumes moreenergy than Skype andWhatsApp and the likely reason is the codecDuo consistently consumes more energy in the presence or absenceof noise However a group conversation may have 2-3 time energysavings when most of the participants suppress silence by mutingthe microphonesNetwork Flow Properties Facebook Viber andWhatsApp adapttheir traffic and flow properties according to conversation patternsFacebook is the best alternative for conversations in a quiet placewhereas Viber and WhatsApp are suitable for noisy places consid-ering data savings and energy consumption respectively Subse-quently the VoIP traffic from these applications is network friendlyas much as VoLTE as we demonstrate The usage of Bluetooth lowenergy (BLE) earbuds and changes in network type ie WiFiLTEdo not affect the performance of the applications

2 EXPERIMENTSWe analyze silence suppression noise resiliency energy consump-tion and network friendliness in mobile VoIP through carefullycontrolled benchmarks conducted using a representative set ofapplications and conversation contexts In the following we de-scribe the applications considered in our study acoustic analysisof overall experimental setup and the tools used to collect variousmeasurement data

21 VoIP ApplicationsWe analyse codec performance by considering five popular mobileVoIP applicationsSkype establishes P2P sessions for one-to-one calls It relies on aserver for conferencing calls with multiple participants The net-work consists of two types of nodes ordinary hosts running Skypeand supernodes acting as gateways to the Skype network Any nodewith the public IP address and adequate resources could operateas a supernode At present there are a fixed number of dedicatedsupernodes operated by Skype in the cloud Traffic is transmitted di-rectly over UDP unless one of the devices is behind a port-restrictedNAT and UDP-restricted firewall in which case traffic is routedthrough the super nodes [19] The latest Skype applications useOpus codec for encoding voice [68]Viber uses dedicated and dynamically allocated servers to mediatethe communication between the call participants [49] Similarlyto Skype signaling takes place over TCP The media is encodedwith IP-MR codec [1] and UDP packets are exchanged through thedynamically assigned serversWhatsApp uses similar client-server architecture as Viber Thecall signaling takes place over TCP and the media is transmittedover UDP Similar to Skype it uses Opus [42]Facebook Messenger amp Duo use WebRTC for VoIP call connec-tions WebRTC [36] relies on the applications for P2P call setupand media is exchanged directly between the participants overUDP [55] WebRTC integrates several codecs such as Opus iSACG711 and G722 [2] The Facebook messenger and Duo may useany of these codecs depending on the platform and peer




Distance 15 meters

Speech generator

callee caller

speech signal


caller

speech signal

callee

speech signal

callee

callee

Room A Room B


Speech generator








0 10 20 30 40 50 60time (s)

-1

-05

0

05

Am

plitu

de

a

0 1 2 3duration (s)

0

02

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1

F(x

)

b

pauses

speech

0 10 20 30 40 50 60

time (s)

30

40

50

60

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80

90

dB

A

c

noiseless

moderate-noise

extreme-noise

speech signal










0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5kWhatsapp

0

5k

bits

100

ms

Viber

0

5kFacebook

0

5k

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15k

20kSkype

0

5k

10k

15kDuo

0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude

speech signal







Nexus 6

Facebook Viber0

5

10

15

20

25a

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p

au

se

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n (

s)

Nexus 6


20

40

60

80

avg

b

itra

te (

kb

ps)





0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5k WhatsApp

0

5k

bits

100

ms

Viber0

5k

10kFacebook















0 100 200 3000

02

04

06

08

1

CD

F

Skype


0

02

04

06

08

1

CD

F


0 100 200 3000

02

04

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08

1Whatsapp

0 100 200 3000

02

04

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1Facebook

0 100 200 3000

02

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1Duo



pktgap(ms)

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bitrate(kbps)

pktgap(ms)

pktsize(bytes)








iPhone 6

Facebook Viber0

5

10

15

avg

p

au

se

du

ratio

n (

s)

iPhone 6


20

40

60

80

100

avg

b

itra

te (

kb

ps)










1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References




Distance 15 meters

Speech generator

callee caller

speech signal


caller

speech signal

callee

speech signal

callee

callee

Room A Room B


Speech generator








0 10 20 30 40 50 60time (s)

-1

-05

0

05

Am

plitu

de

a

0 1 2 3duration (s)

0

02

04

06

08

1

F(x

)

b

pauses

speech

0 10 20 30 40 50 60

time (s)

30

40

50

60

70

80

90

dB

A

c

noiseless

moderate-noise

extreme-noise

speech signal










0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5kWhatsapp

0

5k

bits

100

ms

Viber

0

5kFacebook

0

5k

10k

15k

20kSkype

0

5k

10k

15kDuo

0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude

speech signal







Nexus 6

Facebook Viber0

5

10

15

20

25a

vg

p

au

se

du

ratio

n (

s)

Nexus 6


20

40

60

80

avg

b

itra

te (

kb

ps)





0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5k WhatsApp

0

5k

bits

100

ms

Viber0

5k

10kFacebook















0 100 200 3000

02

04

06

08

1

CD

F

Skype


0

02

04

06

08

1

CD

F


0 100 200 3000

02

04

06

08

1Whatsapp

0 100 200 3000

02

04

06

08

1Facebook

0 100 200 3000

02

04

06

08

1Duo



pktgap(ms)

pktsize(bytes)

bitrate(kbps)

pktgap(ms)

pktsize(bytes)








iPhone 6

Facebook Viber0

5

10

15

avg

p

au

se

du

ratio

n (

s)

iPhone 6


20

40

60

80

100

avg

b

itra

te (

kb

ps)










1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References


0 10 20 30 40 50 60time (s)

-1

-05

0

05

Am

plitu

de

a

0 1 2 3duration (s)

0

02

04

06

08

1

F(x

)

b

pauses

speech

0 10 20 30 40 50 60

time (s)

30

40

50

60

70

80

90

dB

A

c

noiseless

moderate-noise

extreme-noise

speech signal










0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5kWhatsapp

0

5k

bits

100

ms

Viber

0

5kFacebook

0

5k

10k

15k

20kSkype

0

5k

10k

15kDuo

0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude

speech signal







Nexus 6

Facebook Viber0

5

10

15

20

25a

vg

p

au

se

du

ratio

n (

s)

Nexus 6


20

40

60

80

avg

b

itra

te (

kb

ps)





0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5k WhatsApp

0

5k

bits

100

ms

Viber0

5k

10kFacebook















0 100 200 3000

02

04

06

08

1

CD

F

Skype


0

02

04

06

08

1

CD

F


0 100 200 3000

02

04

06

08

1Whatsapp

0 100 200 3000

02

04

06

08

1Facebook

0 100 200 3000

02

04

06

08

1Duo



pktgap(ms)

pktsize(bytes)

bitrate(kbps)

pktgap(ms)

pktsize(bytes)








iPhone 6

Facebook Viber0

5

10

15

avg

p

au

se

du

ratio

n (

s)

iPhone 6


20

40

60

80

100

avg

b

itra

te (

kb

ps)










1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References


0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5kWhatsapp

0

5k

bits

100

ms

Viber

0

5kFacebook

0

5k

10k

15k

20kSkype

0

5k

10k

15kDuo

0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude

speech signal







Nexus 6

Facebook Viber0

5

10

15

20

25a

vg

p

au

se

du

ratio

n (

s)

Nexus 6


20

40

60

80

avg

b

itra

te (

kb

ps)





0 10 20 30 40 50 60 70time (s)

-1

0

1

Ampl

itude speech signal

0

5k WhatsApp

0

5k

bits

100

ms

Viber0

5k

10kFacebook















0 100 200 3000

02

04

06

08

1

CD

F

Skype


0

02

04

06

08

1

CD

F


0 100 200 3000

02

04

06

08

1Whatsapp

0 100 200 3000

02

04

06

08

1Facebook

0 100 200 3000

02

04

06

08

1Duo



pktgap(ms)

pktsize(bytes)

bitrate(kbps)

pktgap(ms)

pktsize(bytes)








iPhone 6

Facebook Viber0

5

10

15

avg

p

au

se

du

ratio

n (

s)

iPhone 6


20

40

60

80

100

avg

b

itra

te (

kb

ps)










1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References













0 100 200 3000

02

04

06

08

1

CD

F

Skype


0

02

04

06

08

1

CD

F


0 100 200 3000

02

04

06

08

1Whatsapp

0 100 200 3000

02

04

06

08

1Facebook

0 100 200 3000

02

04

06

08

1Duo



pktgap(ms)

pktsize(bytes)

bitrate(kbps)

pktgap(ms)

pktsize(bytes)








iPhone 6

Facebook Viber0

5

10

15

avg

p

au

se

du

ratio

n (

s)

iPhone 6


20

40

60

80

100

avg

b

itra

te (

kb

ps)










1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References



1

2

3

avg byte

s

105










20

40

60

avg

b

itra

te (

kb










30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References


30 40 50 60 70 80 90 100 110 120 130 140time (s)

1

20

40

60

80

100

bitra

te (k

bps)














0

02

04

06

08


Skype (64QAM)

WhatsApp (64QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)


0

02

04

06

08

1

CD

F

muted turns

Skype (QPSK)

WhatsApp (16QAM)

Viber (16QAM)

Duo (64QAM)

Facebook (16QAM)






(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References


(a)

(b)



100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)










100

200

300

400

500

600

700

avg

cu

rre

nt

(mA

)







muted turns(mA)



































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References































































































Abstract

1 Introduction

2 Experiments










35 Summary




43 Summary




53 Summary

6 Discussion

7 Related Work

8 Conclusions

References

the bits of silence : redundant traffic in voip

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