1. ETH Zurich – Distributed Computing Group Samuel Welten 1ETH Zurich – Distributed Computing Group Michael Kuhn Roger Wattenhofer Samuel Welten TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAA A A A Social Audio Features for Advanced Music Retrieval Interfaces

2. Ramesh Jain: „I have 20,000 photos on my hard drive and no means to manage them.“ Ramesh Jain: „I have 20,000 photos on my hard drive and no means to manage them.“

3. „Today, I would like to listen to something cheerful.“ „Something like Lenny Kravitz would be great.“ „Who can help me to discover my collection?“

7. …well reflects perceived music similarity. …is as convenient to use as an audio feature space. Solution: Derive a music similarity space from user-generated data. Solution: Derive a music similarity space from user-generated data. We want to have something that… Social Audio Features

10. Item-to-Item Collaborative Filtering „Users who like soccer balls also like vuvuzelas“

12. combine information Meaningful labels But sparse data Meaningful labels But sparse data Good similarity information but no labels Good similarity information but no labels

13. ETH Zurich – Distributed Computing Group Samuel Welten 13 Combining Usage Data and Social Tags

14. … … rock pop female american Madonna – like a prayer Applying Probabilistic Latent Semantic Analysis to Music and Tags rock 60‘s Classic rock american Beatles – hey jude rock punk guitar american Greenday – basket case rock guitar american metal hard pop piano 90‘s female happy melancholic oldies soul male blues Latent class probababilities can be interpreted as coordinates Latent class probababilities can be interpreted as coordinates

15. 32 dimensional audio coordinates for over 1 million songs Each direction is a latent music concept Each point characterizes a style of music

16. Socially derived music similarity + PLSA embedding = Social Audio Features

17. ETH Zurich – Distributed Computing Group Samuel Welten 17 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 Advantages of a Map

18. ETH Zurich – Distributed Computing Group Samuel Welten 18 Evaluation Artist clustering Comparison to collaborative filtering Comparison to collaborative filtering Tag consistency convenient basis to build music software

22. After only few skips, we know pretty well which songs match the user‘s mood After only few skips, we know pretty well which songs match the user‘s mood 22 Use the map to realize a Smart Shuffle Play Mode

24. ETH Zurich – Distributed Computing Group Samuel Welten 24 User Study When users were searching for music they spent 40% of the time in the music map 19% used the tag cloud regularly Smart shuffle was the predominant play mode We recorded the behavior of 128 persons using museek:

26. ETH Zurich – Distributed Computing Group Samuel Welten 26 Selected Comments from museek Users Your software is a pathetic piece of crap! […] Does a good job learning my tastes[…] […] easy browse and make playlists. Auto play related music is very good. […] easy browse and make playlists. Auto play related music is very good. 넥원 잘돌아갑니다 버벅거리지안고 굿 ui 도 굿이고요 ! [...] Love the ability to automatically play similar music. [...] [...] Love the ability to automatically play similar music. [...] Good potential, but album art is tiny & blurry […] Just got it and want to put more music on my sd card now. Pretty cool once you get the hang of it. Awesome app beating the ipod genius feature and coverflow. […]

27. Thank You! Questions & Comments? Download museek for free in the Android market