content-based music information retrieval in wireless ad-hoc networks
DESCRIPTION
Content-Based Music Information Retrieval in Wireless Ad-hoc Networks. A walk in the park…. song excerpt. propagate. reply. song excerpt. reply. An emerging paradigm in music distribution. The new trend is here: wireless devices that can do much (lots of MHz!) - PowerPoint PPT PresentationTRANSCRIPT
Content-Based Music Information Retrieval in Wireless Ad-hoc
Networks
A walk in the park…
song excerpt
song
exc
erpt
propagate
reply
reply
An emerging paradigm in music distribution
The new trend is here: wireless devices that can do much (lots of MHz!)
The music industry found a blooming application: music has turned into commodity over WWW
How can we extend this success to the new trend of wireless networks?
Is this another way to help piracy? No! Licensed distribution of digital music offers:
minimisation of distribution costs custom orders (track selection) instant delivery (temporal + spatial)
What we need to make this true…
CBMIR for wireless P2P networks:Consider the frequent alteration of the network
topologyOptimise the traffic for the constrained
bandwidths of wireless networks (find effective representations of music data)
Design the routing of music data over the wireless ad-hoc network
Why not existing (wired) solutions?
In wireless ad-hoc networks two nodes can communicate only if in close proximity (in-range).
Network peersparticipate randomly participate for short termchange frequently their location.
These factors cause existing approaches, e.g., indexing, to become inapplicable.
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Background Problem definition Proposed method Experimental results Summary
Mobile ad-hoc networks
Wireless mobile ad-hoc network (MANET)Collection of wireless mobile hosts Temporary networkNO centralised administration NO standard support services
The ad-hoc nature requires path discoveryNeed for routing policies in MANETs
Routing in MANETs Rely on some form of broadcasting, e.g.:
source-initiated on-demand routing protocols hybrid routing protocols
Flooding is the simplest broadcasting approach each node in the network forwards a packet exactly
once generates too many redundant transmissions =>
broadcast storm problem To address flooding
probabilistic approaches deterministic approaches
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Background Problem definition Proposed method Experimental results Summary
Problem definition
Given a mobile client that wants to find music documents that are similar to a query, search all approachable peers in an MANET and return possible answers to the querier.
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Background Problem definition Proposed method Experimental results Summary
Template for CBMIR in MANETs
1. User poses a query 2. Query transformed to a representation form R3. R is broadcasted to all peers in range4. Qualifying sequences (true- and false- positives)
comprise an answer-set5. Answer-sets are broadcast back to the querier6. Resolution of false-positives at:
peers that provide answers intermediate peers the querier
7. Return of actual matches to the user/application
FWD traffic
BWD traffic
Options to represent the query
1. The whole query sequence itself (time domain) Large size
2. The first few coefficients of a frequency-domain transformation:
DFT, DCT, … We choose DWT (Haar) transformation Small size
3. A sample of the query sequence and the first few DWT coefficients
Medium size
Options for false-alarm resolution
1. At the qualifying peers Possible when using the whole query sequence No false-alarms
2. At the querier When choosing representation only with DWT
coefficients False-alarms (many!)
3. At the querier, but intermediate peers help Significantly reduced number of false-alarms Intermediate peers prune many of them
Resulting approaches
Query representation
Resolution FWD Traffic BWD Traffic
CQ minimal(only coeffs)
at querier Good(coeffs are small)
Bad(false positives)
QL Maximal(full query)
at peers Bad(query is large)
Good
(no false positives)
ST Medium(coeffs + sample)
at peers and at querier
(+ pruning in the root)
Good(small sample)
Good(pruning policy)
ST example
1
2
3
4
20%
10%
5%
5
1
2
3
4
20%
10%
5%
5
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Background Problem definition Proposed method Experimental results Summary
Simulation test-bed 100 network nodes 300 songs (various music genres, e.g. pop, greek, rock,
classical) average length 5 min Each song was randomly repeated 4 times Mobility simulator (GSTD)
• Area 4 km• Peer radius 500m• Peer velocity 5km/h
Metrics• average traffic• time 1st and last result were discovered
Experiments
2
Time of 1st & last results vs. Max-hop
Increase in available Max-Hop => more peers examined => longer times
Traffic vs. Max-hop
BWD phase is more demanding for all algorithms
Time of 1st & last results vs. query size
increase in query size => increased processing required for the determination of matching excerpts
Traffic vs. query size
increase in query size => propagation of larger representations
Traffic vs. NF parameter
High NF, limits the effectiveness of the policy for the BWD phase, since most peers are selected at random by this policy
Traffic vs. initial sample factor
Forward traffic increases with increasing sample size
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Background Problem definition Proposed method Experimental results Summary
Summary
Introduced CBMIR application in wireless ad-hoc networks
Recognised new challenges posed by wireless ad-hoc networks.
Proposed a novel algorithm, with twofold optimisation: use of query representation with reducing length, selective policy for routing answers, which performs
additional pruning of traffic. Result:
significant reduction in response times and traffic The examined context does not depend on specific
features and distance measure
Content-Based Music Information Retrieval in Wireless Ad-hoc Networks
Thank you!