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

2. A walk in the park… song excerpt propagate 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 1. User poses a query 2. Query transformed to a representation form R 3. R is broadcasted to all peers in range 4. Qualifying sequences (true- and false- positives) comprise an answer-set 5. Answer-sets are broadcast back to the querier 6. 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

13. 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

14. DWT (Haar) transformation provides simple but yet efficient representation of audio considering: non-uniform frequency resolution, impulsive characteristics (C. Roads and Poli, 1997) The Haar wavelet transformation: is easy to compute incrementally, capable in capturing time dependant properties of data overall multi-resolution representation of signals (Kin-Pong Chan and Yu, 2003)

15. 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

16. Resulting approaches Query representation ResolutionFWD TrafficBWD Traffic CQminimal (only coeffs) at querierGood (coeffs are small) Bad (false positives) QLMaximal (full query) at peersBad (query is large) Good (no false positives) STMedium (coeffs + sample) at peers and at querier (+ pruning in the root) Good (small sample) Good (pruning policy)

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

18. Layout Background Problem definition Proposed method Experimental results Summary

19. 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 1 st and last result were discovered Experiments 2

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

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

22. Time of 1 st & last results vs. Range of query

23. Traffic vs. Range of query

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

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

26. 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

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

28. Layout Background Problem definition Proposed method Experimental results Summary

29. 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

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