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MSc Project Report

Automatic Playlist Generation and Music Library Visualisation with Timbral Similarity Measures

Name: Steven Matthew Lloyd

Student No.: 089555161

Supervisor: Professor Mark Sandler

25 August 2009

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DISCLAIMER

This report is submitted as part requirement for the degree of MSc at the University of

London. It is the product of my own labour except where indicated in the text. The report may

be freely copied and distributed provided the source is acknowledged.

Signature:

Date:

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ACKNOWLEDGEMENT

Thanks to Mark for supervising this project, for allowing me to take the work in the direction

of my choosing, and for reigning me in when my enthusiasm didnt always line up with the

timetable.

Thanks to Henry for helping me refine my ideas and for aid in turning the more unintelligible

sections of this text into a readable document.

Thanks to Craig Finn, Robert Pollard, and Dmitri Shostakovich, among many others, for

combining sound waves in a manner that transcends any scientific interpretation.

Thanks to Mom and Dad for buying the piano and signing me up for lessons way back when.

Lastly and most importantly, thanks to Melanie for agreeing to sell most of our belongings,

move three thousand miles, and live in a closet so I could quit my job and learn about all the

cool things you can do with math and music. Thanks for dealing with at times miserable

living conditions and my lack of free time to entertain. It wouldnt have been possible without

you. I think I owe you one.

ABSTRACT

The size of personal digital music collections has grown significantly over the past decade,

but common consumer media players offer limited options for library browsing and

navigation. Various experimental prototypes implement visualisations derived from both

content-based and contextual song features, but their lack of integration into existing media

software prevents evaluation of such systems across a widespread user base. This project

introduces the first content-based music library visualisation implemented as an extension to

an existing consumer software media player. The SoundBite add-on for Songbird media

player exploits timbral audio similarity measures to facilitate two-dimensional map and

network-based navigation and browsing of personal music collections. The similarity

measures also support automatic playlist generation within Songbird.

Incorporating previous work in timbre modelling, this project evaluates statistical

dimensionality reduction techniques in the audio similarity domain with a focus on

minimising computational requirements. The realised system achieves a high quality two-

dimensional representation of a higher dimensional similarity space without exceeding the

computational or memory constraints imposed by the media player environment.

SoundBite for Songbird provides an entry point for real world consumer adoption of

experimental music browsing and navigation methods that have previously been limited to

prototype systems and small-scale user evaluations. Feedback from the Songbird user

community will provide insight into the merits of content-based visualisation in the digital

music marketplace, and SoundBite will provide users with a new interactive context for

exploration of their music collections.

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TABLE OF CONTENTS

DISCLAIMER.......................................................................................................................... 2!

ACKNOWLEDGEMENT....................................................................................................... 3!

ABSTRACT .............................................................................................................................. 4!

TABLE OF CONTENTS......................................................................................................... 5!

CHAPTER 1: INTRODUCTION........................................................................................... 7!

1.1 Motivation: Moving Toward a Media Player That Listens.............................................. 7!

1.2 Developing the Visualisation ........................................................................................... 7!

1.3 Realising the System: SoundBite for Songbird................................................................ 8!

1.4 Report Structure ............................................................................................................. 10!

CHAPTER 2: BACKGROUND............................................................................................ 11!

2.1 Music Information Retrieval .......................................................................................... 11!

2.2 Song Similarity and Music Recommendation Systems ................................................. 12!

2.2.2 Content-Based Similarity Measures........................................................................ 12!

2.2.3 Collaborative Filtering Approaches ........................................................................ 12!

2.2.4 The Cold Start Problem and Hybrid Recommenders.............................................. 13!

2.2.5 Existing Playlist Generators .................................................................................... 14!

2.2.5 Evaluating Similarity Measures .............................................................................. 15!

2.3 Music Library Visualisation........................................................................................... 16!

2.3.1 Standard Consumer Media Players ......................................................................... 16!

2.3.2 Previous Work in Music Library Visualisation....................................................... 16!

2.4 Summary ........................................................................................................................ 17!

CHAPTER 3: TIMBRE MODELS FOR AUTOMATIC PLAYLIST GENERATION . 18!

3.1 The Nature of Timbre..................................................................................................... 18!

3.2 Timbral Feature Extraction ............................................................................................ 19!

3.2.1 The Cepstral Domain............................................................................................... 19!

3.2.2 The Mel Scale.......................................................................................................... 22!

3.2.3 Mel Frequency Cepstral Coefficients...................................................................... 23!

3.3 Timbre Models and Similarity Measures ....................................................................... 24!

3.3.1 MFCC Timbre Models ............................................................................................ 24!

3.3.2 MFCC Model Similarity ......................................................................................... 24!

3.3.3 Lightweight Similarity Measure.............................................................................. 25!

3.3.4 Weighting the Similarity Measure .......................................................................... 26!

3.4 Automatic Playlist Generation ....................................................................................... 26!

3.5 MFCC Feature Extraction Implementation.................................................................... 27!

3.6 Summary ........................................................................................................................ 29!

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CHAPTER 4: MUSIC LIBRARY VISUALISATION USING DIMENSIONALITY

REDUCTION TECHNIQUES.............................................................................................. 30!

4.1 The Dimensionality Reduction Problem........................................................................ 30!

4.2 MFCC Feature Vector Truncation ................................................................................. 34!

4.3 Principal Components Analysis ..................................................................................... 36!

4.3.1 PCA Method............................................................................................................ 37!

4.3.2 PCA Example Collection Configuration Spaces..................................................... 37!

4.4 Multidimensional Scaling .............................................................................................. 39!

4.4.1 Classical Multidimensional Scaling ........................................................................ 39!

4.4.2 MDS Example Collection Configuration Space ..................................................... 41!

4.4.3 MDS Computational Issues..................................................................................... 43!

4.5 Landmark Multidimensional Scaling ............................................................................. 44!

4.5.1 The LMDS Algorithm............................................................................................. 44!

4.5.2 LMDS Example Collection Configuration Spaces ................................................. 45!

4.5.3 LMDS Parameter Selection..................................................................................... 46!

4.6 Comparison of Reduc