distributed computing group visually and acoustically exploring the high-dimensional space of music...
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DistributedComputing
Group
Visually and Acoustically Exploring the High-Dimensional Space of Music
Lukas BossardMichael KuhnRoger Wattenhofer
SocialCom 2009Vancouver, Canada
2 Michael Kuhn, ETH Zurich @ SocialCom 2009
• Storage media– Vinyl records– Compact cassettes– 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 Michael Kuhn, ETH Zurich @ SocialCom 2009
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 Michael Kuhn, ETH Zurich @ SocialCom 2009
Organization by Similarity
• Our Goals– Mobile application (portable player)– Play songs the user likes– Overview of a collection
• Problems on mobile devices– Limited input– Limited output– Limited processing power– Limited memory
• Solution– Use song coordinates provided by www.musicexplorer.org
5 Michael Kuhn, ETH Zurich @ SocialCom 2009
Which songs are similar?
• Goussevskaia et al., WI 2008:– Each song is positioned in a Euclidean „Map of Music“– Similar songs are close to each other in this Euclidean space
6 Michael Kuhn, ETH Zurich @ SocialCom 2009
The Map of Music
• Based on usage data– „behaviour of the crowd“ – Gathered from social music platform (last.fm)– NO audio-analysis!
• Underlying similarity measure– Item-to-item collaborative filtering (Amazon)
[Linden et al., IEEE Internet Computing]
– „users who listen to song A also listen to song B“
• Coordinates available through webservice– www.musicexplorer.org
7 Michael Kuhn, ETH Zurich @ SocialCom 2009
Hey Jude
Imagine
My Prerogative
I want it that way
Praise you
Galvanize
rock
pop
electronic
Using the Map
• Similar songs are close to each other
• Quickly find nearest neighbors
• Span (and play) volumes
• Create smooth playlists by interpolation
• Visualize a collection
• Low memory footprint– Well suited for mobile domain
convenient basis to build music software
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Contributions
Visual and acoustic guide to the
high-dimensional music galaxy
Proof-of-concept application for Android devices („Google-phone“)
11 Michael Kuhn, ETH Zurich @ SocialCom 2009
The Reference: SensMe (Sony Ericsson)
slow
fast
happysad
Create playlist by selecting
areas
Based on audio-analysis
12 Michael Kuhn, ETH Zurich @ SocialCom 2009
Requirements
Global Overview
Local Overview
Orientation
Our problem: 10 dimensions!
13 Michael Kuhn, ETH Zurich @ SocialCom 2009
Lens Metaphor
Few details in the border rings
Detailed view in the center
14 Michael Kuhn, ETH Zurich @ SocialCom 2009
Lens: Recursive Clustering
High resolution in the center
Few details in the border regions
16 Michael Kuhn, ETH Zurich @ SocialCom 2009
The Visual Exploration Interface
• Browsing– Touch cluster to bring it to the
center
• Playlist Generation– Select a number of seed songs– Playlist will consist of songs
around these seeds– Similar to SensME (but songs are
selected in a different interface)
Touch to make this area the new
center
17 Michael Kuhn, ETH Zurich @ SocialCom 2009
Evaluation (1)
• User Experiment– 9 participants– Collection (1400 songs)– 5 minutes to create playlist of 20 songs (for both systems)
• Evaluation: Participants had to...– ...rate each individual song in the playlists– ...fill in a questionaire
vs.
18 Michael Kuhn, ETH Zurich @ SocialCom 2009
Evaluation (2)
• Average song rating (scale: 0..10):– 5.5 (SensMe)– 6.3 (this paper)
• Questionaire (scale: 1..5):
SensMe This paper
Playlist (overall) 2.4 3.3
Diversity (3 is best) 2.4 3.4
Usability 4.7 3.7
Underlying space 2.4 4.0
Use again? 44% 67%
Trade-off:
Accurracy of high-dimensional space versus simplicity of interface
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Idea
Shuffle
(play songs in random order)
Can we do better? Yes!Idea: Learn on the fly which songs the user likes!
Skip = bad songListen = good song
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Realization
Basic algorithm: Voronoi TesselationFirst song was skipped
Supposed to be the user‘s region of
interest
22 Michael Kuhn, ETH Zurich @ SocialCom 2009
Improvements
• Weighting– Account for strong/weak feedback
• Aging– Allows to adapt to changing mood
• Centering– Border regions are risky => go to center
• Escaping– Sometimes play random song to avoid getting stuck somewhere
Rating bar (left = skip, right =
good)
23 Michael Kuhn, ETH Zurich @ SocialCom 2009
References
• Random shuffling (e.g. iPod-Shuffle)
• Pampalk et al. (ISMIR, 2005)– Designed for (Euclidean) audio feature spaces
dg
db
If there are songs with dg < db:
select such song with smallest dg
Else:
select song with largest ratio dg/db
24 Michael Kuhn, ETH Zurich @ SocialCom 2009
Evaluation
• 9 Participants• Song ratings are used as input and for evaluation
Diversity clearly better than Pampalk
Ratings clearly better than
random