1. Social Audio Features for Advanced Music Retrieval Interfaces
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2. „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?“

6. Audio Analysis
Usage Data

7. Social Audio Features We want to have something that…
…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.
Social Audio Features

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

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

13. Combining Usage Data and Social Tags

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

15. 32 dimensional audio coordinates for over 1 million songs
Each direction is a latent music concept
Each point characterizes a style of music
Basket case
Hey jude
Like a prayer

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

17. Advantages of a Map rock electronic pop
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
rock
pop
electronic
Hey Jude
Imagine
My Prerogative
I want it that way
Praise you
Galvanize

18. convenient basis to build music software
Evaluation
Artist clustering
Tag consistency
convenient basis to build music software
Comparison to
collaborative filtering

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

24. We recorded the behavior of 128 persons using museek:
User Study
We recorded the behavior of 128 persons using museek:
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

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

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