Content-Based Music Information Retrieval in Wireless Ad-hoc Networks

1. Content-Based Music Information Retrieval in Wireless Ad-hoc Networks

2. A walk in the park… song excerpt propagate reply song excerpt reply

3. 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)

4. What we need to make this true… • 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

5. Why not existing (wired) solutions? • In wireless ad-hoc networks two nodes can communicate only if in close proximity (in-range). • Network peers • participate randomly • participate for short term • change frequently their location. • These factors cause existing approaches, e.g., indexing, to become inapplicable.

6. Layout • Background • Problem definition • Proposed method • Experimental results • Summary

7. Mobile ad-hoc networks • Wireless mobile ad-hoc network (MANET) • Collection of wireless mobile hosts • Temporary network • NO centralised administration • NO standard support services • The ad-hoc nature requires path discovery • Need for routing policies in MANETs

8. 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

9. Layout • Background • Problem definition • Proposed method • Experimental results • Summary

10. 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.

11. Layout • Background • Problem definition • Proposed method • Experimental results • Summary

12. Template for CBMIR in MANETs • User poses a query • Query transformed to a representation formR • 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 FWD traffic BWD traffic

13. Options to represent the query • The whole query sequence itself (time domain) • Large size • The first few coefficients of a frequency-domain transformation: • DFT, DCT, … • We choose DWT (Haar) transformation • Small size • A sample of the query sequence and the first few DWT coefficients • Medium size

14. Options for false-alarm resolution • At the qualifying peers • Possible when using the whole query sequence • No false-alarms • At the querier • When choosing representation only with DWT coefficients • False-alarms (many!) • At the querier, but intermediate peers help • Significantly reduced number of false-alarms • Intermediate peers prune many of them

15. Resulting approaches

16. 10% 3 5% 4 5% 4 10% 3 5 20% 2 20% 2 1 1 5 ST example

17. Layout • Background • Problem definition • Proposed method • Experimental results • Summary

18. Experiments • 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 2

19. Time of 1st & last results vs. Max-hop Increase in available Max-Hop => more peers examined => longer times

20. Traffic vs. Max-hop BWD phase is more demanding for all algorithms

21. Time of 1st & last results vs. query size increase in query size => increased processing required for the determination of matching excerpts

22. Traffic vs. query size increase in query size => propagation of larger representations

23. 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

24. Traffic vs. initial sample factor Forward traffic increases with increasing sample size

25. Layout • Background • Problem definition • Proposed method • Experimental results • Summary

26. 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

27. Content-Based Music Information Retrieval in Wireless Ad-hoc Networks Thank you!