IIIA - CSIC

Takingpeople

back into social Web music

Claudio Baccigalupo – April 2009

Timeline and motivation

Poolcasting Web Radio (30’)

MySpace Robot (15’)

Q&A and Demo (15’)

“!e beauty of the Internet is that it connects people. !e value is in the other people. If we start to believe that the Internet itself is an entity that has something to say, we’re

devaluing those people and making ourselves into idiots.”

– Jaron Lanier (Computer scientist, composer, visual artist)

A social music experience

“Share” a radio channel?

Authoritative Web Radios

Personalised Recommenders

“Share” a radio channel?

Authoritative Web Radios

Personalised Recommenders

“Share” a radio channel?

Authoritative Web Radios

Group-customised Web radio channels

POOLCASTING WEB RADIO

What is Poolcasting?

A Poolcasting radio channel

Listeners can play music

Listeners can create public channels

Participants contribute with own music

Listeners can meet other listeners

Listeners influence the music played

How to satisfy a group of listeners?

Variety

the same song or songs by the same artist should not be

repeated closely on a channel

How to satisfy a group of listeners?

Variety

the same song or songs by the same artist should not be

repeated closely on a channel

Smoothness

each song should be musically associated with the previous

song played in the channel

Customisation

each song should match the musical preferences of the

current listeners

How to satisfy a group of listeners?

Variety

the same song or songs by the same artist should not be

repeated closely on a channel

Smoothness

each song should be musically associated with the previous

song played in the channel

Customisation

each song should match the musical preferences of the

current listeners

How to satisfy a group of listeners?

Variety

the same song or songs by the same artist should not be

repeated closely on a channel

Smoothness

each song should be musically associated with the previous

song played in the channel

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

Customisation

each song should match the musical preferences of the

current listeners

Smoothness

each song should be musically associated with the previous

song played in the channel

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

How to fulfil the required properties?

Variety

exclude from the channel any recently played song or artist

How to fulfil the required properties?

How to automatically acquire knowledge about musical associations?

Customisation

each song should match the musical preferences of the

current listeners

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

Variety

exclude from the channel any recently played song or artist

Smoothness

which songs and artists are “musically associated”?

Knowledge about musical associations

Poolcasting extracts this knowledge from a set of 993,825 playlists compiled by the users of MusicStrands

Playlists are sequences of songs ordered according to musical, social and cultural criteria that are not discoverable with acoustic-based analysis

!e more the playlists where two songs or artists co-occur, the smaller the distance at which they occur, and the smaller the number of playlists where only one of the two occurs, the higher their musical association

DRG layout of co-occurrences of songs in a set of 993,825 MusicStrands playlists

Knowledge about musical associations

DRG layout of co-occurrences of songs in a set of 106,144 Last.fm playlists

Knowledge about musical associations

Top associated songs for Smoke on the Water (Deep Purple):

Top associated artists for ABBA:

Similar artists for ABBA (Last.fm):

Similar artists for ABBA (All Music Guide):

Space Truckin’ (VV.AA.) Cold Metal (Iggy Pop) Iron Man (Black Sabbath) China Groove (!e

Doobie Brothers) Crossroads (E. Clapton) Sunshine of your love (Cream) Wild !ing (J. Hendrix)

Knowledge about musical associations

Agnetha Fältskog A-Teens Chic Gloria Gaynor !e 5th Dimension Andy Gibb

Agnetha Fältskog Frida Boney M. Bee Gees Olivia Newton-John Baccara

Ace of Base Gemini Maywood Bananarama Lisa Stans"eld Gary Wright Roxette

Customisation

each song should match the musical preferences of the

current listeners

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

How to fulfil the required properties?

Variety

exclude from the channel any recently played song or artist

Smoothness

which songs and artists are “musically associated”?

How to fulfil the required properties?

How to automatically acquire knowledge about the musical preferences of the listeners?

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

Variety

exclude from the channel any recently played song or artist

Smoothness

which songs and artists are “musically associated”?

Customisation

which songs and artists the audience would like to hear?

Knowledge about musical preferences

Explicit preferences for songs played or scheduled on a radio channel can be stated using the Web interface

Knowledge about musical preferences

Implicit preferences of participants for songs in their shared libraries can be inferred combining rating and play count

Fairness

all the listeners of a channel should equally have an

enjoyable music experience

How to fulfil the required properties?

Variety

exclude from the channel any recently played song or artist

Smoothness

which songs and artists are “musically associated”?

Customisation

which songs and artists the audience would like to hear?

How to aggregate multiple preferences over time and satisfy all the listeners of a channel?

Variety

exclude from the channel any recently played song or artist

Smoothness

which songs and artists are “musically associated”?

Customisation

which songs and artists the audience would like to hear?

Fairness

how to create a musical sequence that everyone likes?

The music selection algorithm

Shared Libraries Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)

?

The music selection algorithm

Shared Libraries Music Pool Channel Pool (Rock) Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)

?

The music selection algorithm

Shared Libraries Music Pool Channel Pool (Rock) Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)

?

Retrieve candidate songs musically associated

with the last song played

Musical preferencesBest ranked candidate

among current listeners

The music selection algorithm

Shared Libraries Music Pool Channel Pool (Rock) Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)Retrieve candidate songs

musically associated with the last song played

!ree listeners have diverging individual preferences over which song to play after Woman in Chains (Tears For Fears)

Preference aggregation

-0.6

possible candidates

0

0.8

0.2

0.2

0.2

0.4

0.6

1

0.4

0

-1

aggregatedpreferences

?

?

?

?

!ree listeners have diverging individual preferences over which song to play after Woman in Chains (Tears For Fears)

Preference aggregation

-0.6

possible candidates

0

0.8

0.2

0.2

0.2

0.4

0.6

1

0.4

0

-1

aggregatedpreferences

?

?

?

?

To avoid misery, candidate songs that any listener “dislikes” automatically get the lowest group preference degree

Preference aggregation

-0.6

possible candidates

0

0.8

0.2

0.2

0.2

0.4

0.6

1

0.4

0

-1

aggregatedpreferences

?

?

?

-1

To avoid misery, candidate songs that any listener “dislikes” automatically get the lowest group preference degree

Preference aggregation

-0.6

possible candidates

0

0.8

0.2

0.2

0.2

0.4

0.6

1

0.4

0

-1

aggregatedpreferences

?

?

?

-1

To ensure fairness, the group preference for the remaining candidates equals to the average of the individual preferences

Preference aggregation

-0.6

possible candidates

0

0.8

0.2 0.6 -1

aggregatedpreferences

0.2

0.2

0.4

-1

0.2 1

0.2

0.4

0.4

0

!e highest ranked song is selected to be played next, leaving some listeners more satis"ed than others

Preference aggregation

possible candidates

0.8

aggregatedpreferences

0.2

0.2

0.4

-1

0.4 0

!e highest ranked song is selected to be played next, leaving some listeners more satis"ed than others

Preference aggregation

✓

possible candidates

0.8

aggregatedpreferences

0.2

0.2

0.4

-1

0.4 0

very satis!ed

quite satis!ed

not satis!ed

To achieve fairness in the long run, the preferences of less satis"ed listeners have more in#uence to select the next song

Preference aggregation

1

successive possible

candidates

0

0.6

-0.8

0.3

0.9

0.4

0.3

0.6

0.2

-0.1

-1

aggregatedpreferences

?

?

?

?

Scalability of satisfaction

Scalability of satisfaction

Musical preferences Best ranked candidate among current listeners

The music selection algorithm

Shared Libraries Music Pool Channel Pool (Rock) Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)

?

Retrieve candidate songs musically associated

with the last song played

Individual satisfactions

Musical preferences Best ranked candidate among current listeners

The music selection algorithm

Shared Libraries Music Pool Channel Pool (Rock) Rock Channel

Participants

Everybody Knows(Leonard Cohen)

…

You’re in the air (R.E.M.)

Woman in Chains(Tears For Fears)Retrieve candidate songs

musically associated with the last song played Missing

(Calexico)

Individual satisfactions

The Poolcasting architecture

Participant ParticipantPersonal LibraryMediaPlayer

I N T E R N E T

share library

list ofshared songs

ratings andplay counts

PREFERENCES

MUSIC POOL

availablesongs

Library Parser

MUSICAL ASSOCIATIONSplaylists

CURRENT LISTENERS

CHANNELS

Streaming Server

Stream Generator

list oflisteners

audio signal

OGG stream(256 Kbps)

MP3 stream(64 Kbps)

metadata

rate songs

Song Scheduler

Web Interface

knowledge toschedule

create channel

uploadsong

Database

In favour of the “long tail” of music

Each channel plays a group-customised ordered sequence of songs, adapting in real time to the taste of a changing audience without any e#ort by the listeners

Channels play di$erent songs at di#erent times depending on which libraries are shared and which persons are listening

Whole libraries are exploited, not just the “top of the iceberg”, while musical associations tend to favour uncommon songs, enabling people to discover or re-discover music

!e selection process is able to satisfy an heterogeneous group up, but only under a threshold number of listeners

The future of Poolcasting

MYSPACE ROBOT

Friends as associated musicians

Identifying the most associated artists

Identifying the most associated artists

Expanding the tree of friends

MilesDavis

JohnnyCash

5,123 friends2,123 in common

HankMobley

StanGetz

ColemanHawkins

BobDylan Miles Davis has

27,973 friends

藤田俊亮

Expanding the tree of friends

MilesDavis

JohnnyCash

JakobDylan

Bob Dylan has 189,037 friends,824 shared with Miles Davis

5,123 friends2,123 in common

HankMobley

StanGetz

ColemanHawkins

BobDylan

MattCosta

SherylCrow

藤田俊亮

Expanding the tree of friends

MilesDavis

JohnnyCash

Coleman Hawkins has 261 friends,115 shared with Miles Davis

5,123 friends2,123 in common

HankMobley

StanGetz

ColemanHawkins

BobDylan

CharlieParker

QuincyJones

藤田俊亮

Counting shared connections

MilesDavis

JohnnyCash

5,123 friends2,123 in common

HankMobley

StanGetz

ColemanHawkins

BobDylan

824 common friends (0%)

115 common friends (44%)

2,120 common friends (14%)17 common friends (30%)

44 common friends (31%)

286 common friends (0%)

藤田俊亮

Comparing with Last.fm similar artists

Comparing with Last.fm similar artists

Evaluating music discovery

Q&A AND DEMO

http://www.iiia.csic.es/~claudio

http://github.com/claudiob