1 / 18

From Web to Map: Exploring the World of Music

From Web to Map: Exploring the World of Music. Olga Goussevskaia Michael Kuhn Michael Lorenzi Roger Wattenhofer Web Intelligence 2008 Sydney, Australia. Music in the old da ys. Storage media Vinyl records Compact cassetts Compact discs

mike_john
Download Presentation

From Web to Map: Exploring the World of Music

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. From Web to Map: Exploring the World of Music Olga Goussevskaia Michael Kuhn Michael Lorenzi Roger Wattenhofer Web Intelligence 2008 Sydney, Australia

  2. Music in the old days • 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 organization by album Olga Goussevskaia, ETH Zurich @ Web Intelligence 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! Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  4. Overview • Define music similarity • From Perception to Web • Build a graph of songs • From Web to Map • Embed the graph into Euclidean space • Application prototype: www.musicexplorer.org Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  5. Music Similarity Similar or different??? • Audio content analysis • Metadata analysis • Collaborative filtering • “people who listen to this song also listen to that song” Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  6. From Perception to Web • Data from last.fm (20M users) • Top-50 lists (290K lists, 1.5M distinct songs) • Co-occurrence analysis (normalization cosine(si,sj)=nij/(ninj)1/2) • 1012 (O(TB)!) pair-wise similarity values • Building a graph G • Edge weight w(si,sj) = 1/cosine(si,sj) • Sparsening: co-occ ≥ 2, w(si,sj) ≥ threshold • sim(si, sj) = length(shortestPathG(si, sj)) • Still n = 430K, m = 6.3M, and ever growing • How to operate on G? (assuming G is sparse: m=O(n logn)) • Shortest path computation cost: O(m+logn)=O(n logn) • Memory needed to retrieve one value sim(si, sj): O(m)=O(n logn) Order of seconds on a state-of-the-art PC! Need to store the whole G, even if I only have 50 songs in my collection! Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  7. From Web to Map • Embedding: map vertices of G into points in Euclidean space, s.t. dG/dE (stretch) is “minimized”. • Computation cost of sim(i,j): O(1) time, O(1) memory per item • Embedding algorithms: • Multi Dimensional Scaling (MDS): O(dn2) • Spring embedding (Fruchterman-Reingold): O(n2 + m) • MIS-filtering: O(n log2 Δ) • High-dimensional embedding: O(nl2 + lm) • Landmark MDS (LMDS): O(nld + l3) • Adaptive computation/quality tradeoff • Suitable for dynamic settings Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  8. Iterative Embedding • Assumption: some links erroneously shortcut certain paths E [# random edges] = X • Repeat (X / f) times • embed G (using e.g. LMDS) • Remove (from G) fraction f of edges with highest stretch dE/dG • Example: Kleinberg graph (20x20 grid, f = 0.003) Spring embedding output After 12 rounds After 30 rounds After 6 rounds Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  9. Evaluation • Music Taxonomy (www.allmusic.com) • Control set: 7K songs with genre information How well does the resulting map represent music similarity? Genre distance dS= LCA (least common ancestor) Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  10. Evaluation: Quality Measures • Distance comparison QL: average similarity increase as a function of genre distance ds • Embedding smoothness QR: average # of genre re-occurrences on a random line Avg. similarity of pairs (si,sj) w/ ds(i,j)=h Songs that belong to distant genres should be far away in the embedding. Genre transitions in the embedding should be “smooth”. Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  11. Evaluation: Iterative Embedding (430K nodes, 10 dimensions) After 30 rounds, f=0.5% LMDS output Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  12. Evaluation Closest neighbors in 10D Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  13. Applications: Music Explorer • www.musicexplorer.org • Web service to query coordinates (current DB with 430K titles) • Visualization in 2D • Zoom level according to song popularity • Playlist generation based on trajectories Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  14. Playlist generation • Interpolation between start and end-point • Smooth transition from one style to the other • In reality: 10 dimensions Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  15. 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, ...) • Notion of direction coordinates are well suited for mobile applications coordinates are well suited for similarity based organization Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  16. Towards a new world of music? • Euclidean representation • Efficient similarity computation (time and memory) • No server needed: distributed applications • Building blocks for new functionalities: • New scenarios: • Mobile file sharing • P2P overlay based on the map • Innovations at home • “Play anything hip-hip… not this and not closely related songs… go towards Detroit house, be there in an hour” • Automatic DJ (collect feedback from mobiles, generate playlists based on guests regions of interest) Notion of Direction (Browsing) Volumes (Interest Regions) Trajectories (Playlists) Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  17. Conclusions • Necessary? Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

  18. Thanks for your Attention • Questions? Olga Goussevskaia, ETH Zurich @ Web Intelligence 2008

More Related