1. Distributed Computing Group Exploring Music Collections on Mobile Devices Michael Kuhn Olga Goussevskaia Roger Wattenhofer MobileHCI 2008 Amsterdam, NL

2. 2 Michael Kuhn, ETH Zurich @ MobileHCI 2008 2 Storage media –Vinyl records –Compact cassetts –Compact discs An Album is stored on a single physical storage medium –Sequence of songs given by album –Album is typically listened to as a whole History organization by album

3. 3 Michael Kuhn, ETH Zurich @ MobileHCI 2008 3 Music today Huge offer, easily available –filesharing, iTunes, amazon, etc. Large collections –The entire collection is stored on a single electronic storage medium –Organization by albums (and other lists) is no longer appropriate organize by similarity

4. 4 Michael Kuhn, ETH Zurich @ MobileHCI 2008 4 Contributions Vision –Plays songs the user likes –Overview of a collection –Directly on mp3-player (or phone) Problems on mobile devices –Limited input –Limited output –Limited processing power –Limited memory Contribution –Use song coordinates that reflect similarity –Proof-of-concept implementation on Android

5. 5 Michael Kuhn, ETH Zurich @ MobileHCI 2008 5 Music Explorer www.musicexplorer.org –Webservice that provides 10D coordinates for songs –Similar songs are close to each other in Euclidean space –Similarity information based on co-occurrence data –Currently about 400K songs available Similarity derived by means of co-occurrence analysis

6. 6 Michael Kuhn, ETH Zurich @ MobileHCI 2008 6 Music in Euclidean Space Performance –Similarity computation comes almost for free: O(1) time –Memory footprint is extremly low: O(1) per song –All information can be saved in the file, no server connection required. Applications –Trajectories (playlists,...) –Volumes (region of interest,...) –etc. coordinates are well suited for mobile applications coordinates are well suited for similarity based organization

7. 7 Michael Kuhn, ETH Zurich @ MobileHCI 2008 7 Playlist generation Interpolation between start and end-point –Smooth transition from one style to the other –In reality: 10 dimensions

8. 8 Michael Kuhn, ETH Zurich @ MobileHCI 2008 8 Similarity-based Navigation Basic idea: Browse through neighborhood lists Challenges –Reachability: Entire collection should be reachable from any given starting point –Searchability: It should be possible to reach new regions within few steps

9. 9 Michael Kuhn, ETH Zurich @ MobileHCI 2008 9 Similarity-based Navigation (Small-World) J. Kleinberg: The Small-World Phenomenon: An Algorithmic Perspective, STOC’00 –Augmenting a (hyper-)grid with edges following a particular length distribution (d -r, r = #dim) leads to polylog diameter (=>reachability) –Short paths do not only exist, but can be found using local knowledge only (=>searchability)

10. 10 Michael Kuhn, ETH Zurich @ MobileHCI 2008 10 Similarity-based Navigation (Clustering) Idea: Cluster similar songs and list clusters instead of single songs –Cover entire collection (=>reachability) –Small clusters for close-by songs –Large clusters for distant regions (=>searchability)

11. 11 Michael Kuhn, ETH Zurich @ MobileHCI 2008 11 Conclusions and Future Work Embedding songs into Euclidean space opens many possibilities for mobile applications We have presented a proof-of-concept Android application that –can create smooth playlists –allows to browse collections based on smilarity –does not require (expensive) connection to a server or DB Future directions –Visually browsing collections (problem: 10D => 2D) –Playlist generation on the fly –Collaborative features –...

12. 12 Michael Kuhn, ETH Zurich @ MobileHCI 2008 12 Thanks for your Attention Questions?