the musicexplorer project: mapping the world of music
Post on 26-Feb-2016
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Michael Kuhn
Distributed Computing Group (DISCO)ETH ZurichThe MusicExplorer Project:Mapping the World of Music
Today, I woud like to listen to something cheerful.Something like Lenny Kravitz would be great.Who can help me to discover my collection?
In my shelf AC/DC isnext to the ZZ Top...
play random songs that match my moodWhats the Talk about?
basic idea: map of music
constructing the map(MDS, PLSA)
using the map(Youtube, Android)Map of Music: What is it good for?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 footprintWell suited for mobile domainAdvantages of a MapHey JudeImagineMy PrerogativeI want it that wayPraise youGalvanizerockpopelectronicconvenient basis to build music softwareHow to Construct a Map of Music?
Similar or different???
Music SimilarityAudio Analysis
Usage Data
From Usage Data to Similarity
Collaborative Filtering
Folksonomies (Tags)Collaborative Filtering and MDSMethod 1:Basic Idead = ?item-to-item collaborative filtering1graph for all-pairsdistances2MDS to embed graph (i.e. distances)into Euclidean space3Item-to-Item Collaborative Filtering
People Who Listen To This Song also Listen to...[Linden et al., 2003]
Users who listen to A also listen to BTop-50 listened songs per userNormalization (cosine similarity)
Pairwise Similarity
#common users (co-occurrences)Occurrences of song AOccurrences of song BGraph EmbeddingEBDCA2334545BAEDCWell-known for dimensionality reductionfirst described by Young and Householder, 1938
Principal Component Analysis (PCA): Project on hyperplane that maximizes variance.Computed by solving an eigenvalue problem.
Basic idea of MDS:Assume that the exact positions y1,...,yN in a high-dimensional space are given.It can be shown that knowing only the distances d(yi, yj) between points we can calculate the same result as applying PCA to y1,...,yN.
Problem: Complexity O(n2 log n)Classical Multidimensional Scaling (MDS)Select k landmarks and embed them using MDS
For the remaining points: Place according to distances from landmarks
Complexity: O(k n log n)Landmark MDS[de Silva and Tenenbaum, 1999]Assumption: some links erroneously shortcut certain paths
Idea: Use embedding as estimator for distanceShortcut edges get stretchedRemove edges with worst stretch and re-embed
Example: Kleinberg graph (20x20 grid with random edges)Iterative Embedding
Original embedding(spring embedder)After 6 roundsAfter 12 roundsAfter 30 rounds19Kleinberg graph: grid edges + one random edge per node (follows 1/d^2 distribution, d=manhattan distance)Evaluation: Dimensionality
Pink Floyd - TimePink Floyd - On the RunPink Floyd - Any Colour you LikePink Floyd - The Great Gig in the SkyPink Floyd - EclipsePink Floyd - Us and ThemPink Floyd - Brain DamagePink Floyd - Speak to MePink Floyd - MoneyPink Floyd - BreathePink Floyd - One of These DaysMiles Davis - So WhatHorace Silver - Song For My FatherBill Evans - All of YouMiles Davis - Freddie FreeloaderNat King Cole - The More I See YouMiles Davis - So NearMiles Davis - Flamenco SketchesCharles Mingus - Eat That ChickenJimmy Smith - On the Sunny SideJulie London - DaddyBill Evans My Mans Gone Now10 Dimensions give a reasonable qualityExample Neighborhoods in 10D Space20Dimensionality: - quality(distorsion)/space(performance) tradeoff - max QR at 10D, in contrast to other work, which focuses on at most 3D (visualization) - The axis are not assigned any semantical meaning (but maybe one could find that out? Open problem)
Neighborhood lists: search a little on whether the jazz list is really good.Social Tags and PLSAMethod 2:
Similarity from Social Tagging
Probabilistic Latent Semantic Analysis (PLSA)w1w2wMZ1 =?Z2 =?ZK =?P(w|z)P(z|d)d1d2dNdocumentslatent semantic classeswordssongslatent music style classestags[Hofmann, 1999]PLSA: Interpretation as Spacew1w2wMZ1 =?Z2 =?ZK =?P(w|z)P(z|d)d1d2dNsongslatent music style classestagscan be seen as a vector that defines a point in space [Hofmann, 1999]K small: Dimensionality reductionPLSA Space
latent class 2latent class 3latent class 1Probabilities sum to 1:K-1 dimensional hyperplaneSimilar documents (songs) are close to each othermusic space: 32 dimensionsAdvantages of LMDS:Same accurracy at lower dimensionality (10 vs. 32)
Advantages of PLSA:Natural meaning of tagsAssignment of tags to songs (probabilistic)LMDS vs. PLSA SpaceCurrent sizes (approx.):LMDS: 600K tracksPLSA: 1.1M tracksUsing the MapVisualization?
high-dimensional!Identify relevant tags
Find centroids of these tags in 10D
Apply Principal Component Analysis (PCA) to these centroidsFrom 10D to 2D
What people have chosen during the researchers night in ZurichYouJuke The YouTube Jukebox
YouTube as media sourceMusic map to create smart playlist33
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Reaching YouJukewww.youjuke.org
apps.facebook.com/youjuke
seek
play random songs that match my mood
In my shelf John Lennon isnext to the Beatles...seek:The map of music on Android
An Intelligent iPod-Shuffleskip = listen = RealizationAfter only few skips, we know pretty well which songs match the users moodWork in Progress: Who is Dancing?
AC/DCBeatlesProdigy
In my shelf AC/DC isnext to the ZZ Top...Browsing Covers
Video
www.musicexplorer.org/museekInternet connection only required at first startup!Thanks to:Lukas Bossard Mihai CalinOlga GoussevskaiaMichael LorenziRoger WattenhoferSamuel Welten
URLs:www.musicexplorer.org/museekwww.youjuke.orgapps.facebook.com/youjuke
E-Mail:kuhnmi@tik.ee.ethz.ch (Michael Kuhn)Questions?
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