cocktail party problem as binary classification
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Cocktail Party Problem as Binary Classification. DeLiang Wang Perception & Neurodynamics Lab Ohio State University. Outline of presentation. Cocktail party problem Computational theory analysis Ideal binary mask Speech intelligibility tests - PowerPoint PPT PresentationTRANSCRIPT
Cocktail Party Problem as Binary Classification
DeLiang Wang
Perception & Neurodynamics LabOhio State University
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Outline of presentation
Cocktail party problem Computational theory analysis
Ideal binary mask Speech intelligibility tests
Unvoiced speech segregation as binary classification
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Real-world auditionWhat?• Speech
messagespeaker
age, gender, linguistic origin, mood, …
• Music• Car passing byWhere?• Left, right, up, down• How close?Channel characteristicsEnvironment characteristics• Room reverberation• Ambient noise
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Sources of intrusion and distortion
additive noise from other sound sources
reverberation from surface reflections
channel distortion
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Cocktail party problem
• Term coined by Cherry• “One of our most important faculties is our ability to listen to, and
follow, one speaker in the presence of others. This is such a common experience that we may take it for granted; we may call it ‘the cocktail party problem’…” (Cherry’57)
• “For ‘cocktail party’-like situations… when all voices are equally loud, speech remains intelligible for normal-hearing listeners even when there are as many as six interfering talkers” (Bronkhorst & Plomp’92)
Ball-room problem by Helmholtz “Complicated beyond conception” (Helmholtz, 1863)
Speech segregation problem
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Approaches to Speech Segregation Problem Speech enhancement
Enhance signal-to-noise ratio (SNR) or speech quality by attenuating interference. Applicable to monaural recordings
Limitation: Stationarity and estimation of interference Spatial filtering (beamforming)
Extract target sound from a specific spatial direction with a sensor array
Limitation: Configuration stationarity. What if the target switches or changes location?
Independent component analysis (ICA) Find a demixing matrix from mixtures of sound sources Limitation: Strong assumptions. Chief among them is stationarity
of mixing matrix
• “No machine has yet been constructed to do just that [solving the cocktail party problem].” (Cherry’57)
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Auditory scene analysis
Listeners parse the complex mixture of sounds arriving at the ears in order to form a mental representation of each sound source
This perceptual process is called auditory scene analysis (Bregman’90)
Two conceptual processes of auditory scene analysis (ASA): Segmentation. Decompose the acoustic mixture into sensory
elements (segments) Grouping. Combine segments into groups, so that segments in the
same group likely originate from the same environmental source
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Computational auditory scene analysis
Computational auditory scene analysis (CASA) approaches sound separation based on ASA principles Feature based approaches Model based approaches
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Outline of presentation
Cocktail party problem Computational theory analysis
Ideal binary mask Speech intelligibility tests
Unvoiced speech segregation as binary classification
10
What is the goal of CASA?
What is the goal of perception? The perceptual systems are ways of seeking and extracting
information about the environment from sensory input (Gibson’66) The purpose of vision is to produce a visual description of the
environment for the viewer (Marr’82) By analogy, the purpose of audition is to produce an auditory
description of the environment for the listener
What is the computational goal of ASA? The goal of ASA is to segregate sound mixtures into separate
perceptual representations (or auditory streams), each of which corresponds to an acoustic event (Bregman’90)
By extrapolation the goal of CASA is to develop computational systems that extract individual streams from sound mixtures
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Marrian three-level analysis
According to Marr (1982), a complex information processing system must be understood in three levels Computational theory: goal, its appropriateness, and basic processing
strategy Representation and algorithm: representations of input and output
and transformation algorithms Implementation: physical realization
All levels of explanation are required for eventual understanding of perceptual information processing
Computational theory analysis – understanding the character of the problem – is critically important
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Computational-theory analysis of ASA
To form a stream, a sound must be audible on its own The number of streams that can be computed at a time is
limited Magical number 4 for simple sounds such as tones and vowels
(Cowan’01)? 1+1, or figure-ground segregation, in noisy environment such as a
cocktail party?
Auditory masking further constrains the ASA output Within a critical band a stronger signal masks a weaker one
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Computational-theory analysis of ASA (cont.)
ASA outcome depends on sound types (overall SNR is 0)Noise-Noise: pink , white , pink+white Tone-Tone: tone1 , tone2 , tone1+tone2Speech-Speech: Noise-Tone:Noise-Speech:Tone-Speech:
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Some alternative CASA goals
Extract all underlying sound sources or the target sound source (the gold standard) Implicit in speech enhancement, spatial filtering, and ICA Segregating all sources is implausible, and probably unrealistic with
one or two microphones
Enhance automatic speech recognition (ASR) Close coupling with a primary motivation of speech segregation Perceiving is more than recognizing (Treisman’99)
Enhance human listening Advantage: close coupling with auditory perception There are applications that involve no human listening
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Ideal binary mask as CASA goal
• Motivated by above analysis, we have suggested the ideal binary mask as a main goal of CASA (Hu & Wang’01, ’04)
• Key idea is to retain parts of a target sound that are stronger than the acoustic background, or discard the rest• What a target is depends on intention, attention, etc.
• The definition of the ideal binary mask (IBM)
s(t, f ): Target energy in unit (t, f ) n(t, f ): Noise energy θ: A local SNR criterion (LC) in dB, which is typically chosen to be 0 dB It does not actually separate the mixture!
otherwise0
),(),( if1),(
ftnftsftIBM
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IBM illustration
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Properties of IBM
Flexibility: With the same mixture, the definition leads to different IBMs depending on what target is
Well-definedness: IBM is well-defined no matter how many intrusions are in the scene or how many targets need to be segregated
Consistent with computational-theory analysis of ASA Audibility and capacity Auditory masking Effects of target and noise types
Optimality: Under certain conditions the ideal binary mask with θ = 0 dB is the optimal binary mask from the perspective of SNR gain
• The ideal binary mask provides an excellent front-end for robust ASR (Cooke et al.’01; Roman et al.’03)
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Subject tests of ideal binary masking
• Recent studies found large speech intelligibility improvements by applying ideal binary masking for normal-hearing (Brungart et al.’06; Li & Loizou’08), and hearing-impaired (Anzalone et al.’06; Wang et al.’09) listeners• Improvement for stationary noise is above 7 dB for normal-hearing
(NH) listeners, and above 9 dB for hearing-impaired (HI) listeners
• Improvement for modulated noise is significantly larger than for stationary noise
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Test conditions of Wang et al.’09
SSN: Unprocessed monaural mixtures of speech-shaped noise (SSN) and Dantale II sentences (0 dB: -10 dB: )
CAFÉ: Unprocessed monaural mixtures of cafeteria noise (CAFÉ) and Dantale II sentences (0 dB: -10 dB: )
SSN-IBM: IBM applied to SSN (0 dB: -10 dB: -20 dB: )
CAFÉ-IBM: IBM applied to CAFÉ (0 dB: -10 dB: -20 dB: )
Intelligibility results are measured in terms of speech reception threshold (SRT), the required SNR level for 50% intelligibility score
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Wang et al.’s results
12 NH subjects (10 male and 2 female), and 12 HI subjects (9 male and 3 female) SRT means for the 4 conditions for NH listeners: (-8.2, -10.3, -15.6, -20.7) SRT means for the 4 conditions for HI listeners: (-5.6, -3.8, -14.8, -19.4)
NH H I
SSN CAFE SSN -IBM CAFE-IBM-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
Dan
tale
II
SR
T (
dB)
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Speech perception of noise with binary gains Wang et al. (2008) found that, when LC is chosen to be the same
as the input SNR, nearly perfect intelligibility is obtained when input SNR is -∞ dB (i.e. the mixture contains noise only with no target speech)
Time (s)
Ce
nte
r F
req
ue
ncy
(H
z)
0.4 0.8 1.2 1.6 2
7743
2489
603
55
96 dB
72 dB
48 dB
24 dB
0 dB
Time (s)
Ce
nte
r F
req
ue
ncy
(H
z)
0.4 0.8 1.2 1.6 2
7743
2489
603
55
Time (s)
Ch
an
ne
l Nu
mb
er
0.4 0.8 1.2 1.6 2
32
22
12
2
Time (s)
Ce
nte
r F
req
ue
ncy
(H
z)
0.4 0.8 1.2 1.6 2
7743
2489
603
55
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Wang et al.’08 results
Despite a great reduction of spectrotemporal information, a pattern of binary gains is apparently sufficient for human speech recognition
Mean numbers for the 4 conditions: (97.1%, 92.9%, 54.3%, 7.6%)
N umber of channels
4 8 16 320
10
20
30
40
50
60
70
80
90
100P
erc
en
t c
orr
ec
t
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Interim summary
Ideal binary mask is an appropriate computational goal of auditory scene analysis in general, and speech segregation in particular
Hence solving the cocktail party problem would amount to binary classification This formulation opens the problem to a variety of pattern
classification methods
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Outline of presentation
Cocktail party problem Computational theory analysis
Ideal binary mask Speech intelligibility tests
Unvoiced speech segregation as binary classification
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Unvoiced speech Speech sounds consist of vowels and consonants;
consonants further consist of voiced and unvoiced consonants
• For English, unvoiced speech sounds come from the following consonant categories:• Stops (plosives)
– Unvoiced: /p/ (pool), /t/ (tool), and /k/ (cake)– Voiced: /b/ (book), /d/ (day), and /g/ (gate)
• Fricatives– Unvoiced: /s/(six), /sh/ (sheep), /f/ (fix), and /th/ (this)– Voiced: /z/ (zoo), /zh/ (pleasure), /v/ (vine), and /dh/ (that)– Mixed: /h/ (high)
• Affricates (stop followed by fricative)– Unvoiced: /ch/ (chicken)– Voiced: /jh/ (orange)
• We refer to the above consonants as expanded obstruents
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Unvoiced speech segregation
• Unvoiced speech constitutes 20-25% of all speech sounds• It carries crucial information for speech intelligibility
• Unvoiced speech is more difficult to segregate than voiced speech• Voiced speech is highly structured, whereas unvoiced speech lacks
harmonicity and is often noise-like
• Unvoiced speech is usually much weaker than voiced speech and therefore more susceptible to interference
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Processing stages of Hu-Wang’08 model
Segmentation
Mixture
Auditory periphery
Segregated speech
Grouping
• Peripheral processing results in a two-dimensional cochleagram
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Auditory segmentation
• Auditory segmentation is to decompose an auditory scene into contiguous time-frequency (T-F) regions (segments), each of which should contain signal mostly from the same sound source• The definition of segmentation applies to both voiced and unvoiced
speech
• This is equivalent to identifying onsets and offsets of individual T-F segments, which correspond to sudden changes of acoustic energy
• Our segmentation is based on a multiscale onset/offset analysis (Hu & Wang’07)• Smoothing along time and frequency dimensions• Onset/offset detection and onset/offset front matching• Multiscale integration
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Smoothed intensity
(a)
Fre
qu
en
cy (
Hz)
0 0.5 1 1.5 2 2.550
363
1246
3255
8000(b)
Fre
qu
en
cy (
Hz)
0 0.5 1 1.5 2 2.550
363
1246
3255
8000
(c)
Fre
qu
en
cy (
Hz)
Time (s) 0 0.5 1 1.5 2 2.5
50
363
1246
3255
8000(d)
Fre
qu
en
cy (
Hz)
Time (s) 0 0.5 1 1.5 2 2.5
50
363
1246
3255
8000
Utterance: “That noise problem grows more annoying each day”Interference: Crowd noise in a playground. Mixed at 0 dB SNRScale in freq. and time: (a) (0, 0), initial intensity. (b) (2, 1/14). (c) (6, 1/14). (d) (6, 1/4)
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Segmentation result
The bounding contours of estimated segments from multiscale analysis. The background is represented by blue:
(a)One scale analysis
(b)Two-scale analysis
(c)Three-scale analysis
(d)Four-scale analysis
(e)The ideal binary mask
(f)The mixture
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Grouping
• Apply auditory segmentation to generate all segments for the entire mixture
• Segregate voiced speech using an existing algorithm
• Identify segments dominated by voiced target using segregated voiced speech
• Identify segments dominated by unvoiced speech based on speech/nonspeech classification• Assuming nonspeech interference due to the lack of sequential
organization
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Speech/nonspeech classification
• A T-F segment is classified as speech if
• Xs: The energy of all the T-F units within segment s
• H0: The hypothesis that s is dominated by expanded obstruents
• H1: The hypothesis that s is interference dominant
)|()|( 10 ss HPHP XX
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Speech/nonspeech classification (cont.)
• By the Bayes rule, we have
• Since segments have varied durations, directly evaluating the above likelihoods is computationally infeasible
• Instead, we assume that each time frame within a segment is statistically independent given a hypothesis
• A multilayer perceptron is trained to distinguish expanded obstruents from nonspeech interference
)()|()()|( 1100 HPHPHPHP ss XX
1)|)((
)|)((
)(
)( 2
1 1
0
1
0
m
mm s
s
HmXP
HmXP
HP
HP
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Speech/nonspeech classification (cont.)
• The prior probability ratio of , is found to be approximately linear with respect to input SNR
• Assuming that interference energy does not vary greatly over the duration of an utterance, earlier segregation of voiced speech enables us to estimate input SNR
)(/)( 10 HPHP
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Speech/nonspeech classification (cont.)
• With estimated input SNR, each segment is then classified as either expanded obstruents or interference
• Segments classified as expanded obstruents join the segregated voiced speech to produce the final output
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(a) Clean utteranceF
requency
(H
z)
0.5 1 1.5 2 2.550
363
1246
3255
8000
(c) Segregated voiced utterance
Fre
quency
(H
z)
0.5 1 1.5 2 2.550
363
1246
3255
8000
(b) Mixture (SNR 0 dB)
0.5 1 1.5 2 2.5
(d) Segregated whole utterance
0.5 1 1.5 2 2.5
(e) Utterance segregated from IBM
Fre
quency
(H
z)
Time (S)0.5 1 1.5 2 2.5
50
363
1246
3255
8000
Example of segregation
Utterance: “That noise problem grows more annoying each day”Interference: Crowd noise in a playground (IBM: Ideal binary mask)
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SNR of segregated target
-5 0 5 10 15
0
5
10
15
(a)
Ove
rall
SN
R (dB
) Proposed systemSpectral subtraction
0 5 10 15-10
-5
0
5
10
(b)
SN
R in
unvo
iced fr
am
es
(dB
)
Mixture SNR (dB)
Compared to spectral subtraction assuming perfect speech pause detection
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Conclusion
• Analysis of ideal binary mask as CASA goal
• Formulation of the cocktail party problem as binary classification
• Segregation of unvoiced speech based on segment classification• The proposed model represents the first systematic study on
unvoiced speech segregation
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Credits
• Speech intelligibility tests of IBM: Joint with Ulrik Kjems, Michael S. Pedersen, Jesper Boldt, and Thomas Lunner, at Oticon
• Unvoiced speech segregation: Joint with Guoning Hu