ETH Zurich – Distributed Computing Group Samuel Welten 1ETH Zurich – Distributed Computing Group

Michael KuhnRoger Wattenhofer

Samuel Welten

Social Audio Features for Advanced Music Retrieval Interfaces

Ramesh Jain:

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

„Today, I would like to listen to something cheerful.“

„Something like Lenny Kravitz would be great.“

„Who can help me to discover my collection?“

Audio Analysis Usage Data

…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.

We want to have something that…

Social Audio Features

Item-to-Item Collaborative Filtering

„Users who like soccer balls also like vuvuzelas“

combine information

Meaningful labels But sparse data

Good similarity informationbut no labels

ETH Zurich – Distributed Computing Group Samuel Welten 13

Combining Usage Data and Social Tags

…

rockpop

female

american

Madonna – like a prayer

Applying Probabilistic Latent Semantic Analysis to Music and Tags

rock60‘s

Classic rock

american

Beatles – hey jude

rockpunk

guitar

american

Greenday – basket case

rockguitar

american

metal

hard

poppiano

90‘s

female

happy

melancholicoldies

soul

male

blues

Latent class probababilities can be interpreted as coordinates

• 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

Socially derived music similarity

+

PLSA embedding

=

Social Audio Features

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• 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

Hey Jude

Imagine

My Prerogative

I want it that way

Praise you

Galvanize

rock

pop

electronic

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Evaluation

Artist clustering

Comparison to collaborative filtering

Tag consistency

convenient basis to build music software

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

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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:

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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. Autoplay related music is

very good.넥원 잘돌아갑니다 버벅거리지안고 굿

ui 도 굿이고요 ![...] 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. […]

Thank You!Questions & Comments?

Download museek for free in the Android market