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Content-Based Music
Information Retrieval
in Wireless Ad-hoc
Networks
A walk in the park…
An emerging paradigm in
music distribution
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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…
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CBMIR for wireless P2P networks:
Consider the frequent alteration of the network
topology
 Optimise 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
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Why not existing (wired)
solutions?
In wireless ad-hoc networks two nodes can
communicate only if in close proximity (inrange).
 Network peers
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participate randomly
 participate for short term
 change frequently their location.
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These factors cause existing approaches,
e.g., indexing, to become inapplicable.
Layout
Background
 Problem definition
 Proposed method
 Experimental results
 Summary
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Mobile ad-hoc networks
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Wireless mobile ad-hoc network (MANET)
Collection of wireless mobile hosts
 Temporary network
 NO centralised administration
 NO standard support services
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The ad-hoc nature requires path discovery
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Need for routing policies in MANETs
Routing in MANETs
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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
Layout
Background
 Problem definition
 Proposed method
 Experimental results
 Summary
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Problem definition
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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.
Layout
Background
 Problem definition
 Proposed method
 Experimental results
 Summary
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Template for CBMIR in
MANETs
1.
FWD
traffic
2.
3.
4.
BWD
traffic
5.
6.
7.
User poses a query
Query transformed to a representation form R
R is broadcasted to all peers in range
Qualifying sequences (true- and false- positives)
comprise an answer-set
Answer-sets are broadcast back to the querier
Resolution of false-positives at:
 peers that provide answers
 intermediate peers
 the querier
Return of actual matches to the user/application
Options to represent the
query
1.
The whole query sequence itself (time domain)
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2.
The first few coefficients of a frequency-domain
transformation:
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3.
Large size
DFT, DCT, …
We choose DWT (Haar) transformation
Small size
A sample of the query sequence and the first
few DWT coefficients
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Medium size
Options for false-alarm
resolution
1.
At the qualifying peers
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2.
At the querier
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3.
Possible when using the whole query sequence
No false-alarms
When choosing representation only with DWT
coefficients
False-alarms (many!)
At the querier, but intermediate peers help
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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
10% 3
5% 4
5% 4
10% 3
5
20% 2
20% 2
1
5
1
Layout
Background
 Problem definition
 Proposed method
 Experimental results
 Summary

Experiments
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Simulation test-bed
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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 2
• Peer radius 500m
• Peer velocity 5km/h
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Metrics
• average traffic
• time 1st and last result were discovered
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
Layout
Background
 Problem definition
 Proposed method
 Experimental results
 Summary
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Summary
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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!