Distributed Computing Group Exploring Music Collections on Mobile Devices Michael Kuhn Olga...

DistributedComputing

Group

Exploring Music Collections on Mobile Devices

Michael KuhnOlga GoussevskaiaRoger Wattenhofer

MobileHCI 2008Amsterdam, NL

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


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


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


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 ofco-occurrence analysis


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


Playlist generation

• Interpolation between start and end-point– Smooth transition from one style to the other

– In reality: 10 dimensions


Hey Jude (Beatles)

Yesterday (Beatles)

Imagine (John Lennon)

...

Massachusetts (Bee Gees)


World (Bee Gees)

Odessa (Bee Gees)

...

Pet Sounds (Beach Boys)

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

Hey Jude (Beatles)

Yesterday (Beatles)

Imagine (John Lennon)

...



World (Bee Gees)

Odessa (Bee Gees)

...

Pet Sounds (Beach Boys)

d > r

r


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)


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)


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– ...


Thanks for your Attention

• Questions?

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