Audio Fingerprinting

MUMT 611

Ichiro Fujinaga

McGill University

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Introduction

Fingerprints uniquely identify people Audio fingerprints aims to uniquely identify

a piece of music from a short excerpt of the music

Other names:Acoustic fingerprintingContent-based audio identification

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Applications

“The popular social networking site MySpace.com announced Monday that it has licensed technology [Gracenote] that will help it prevent unauthorized copyrighted music from being posted to MySpace users’ pages.” Macworld (2006/10/06)

“Adding missing album art:With the increased emphasis on album art, Windows Media Player 11 also ensures that missing album art isn't a problem. Most album art can automatically be populated in the background using the advanced audio fingerprinting capabilities in Windows Media Player 11.”

www.microsoft.com/windows/windowsmedia/player/11

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Commercial products

Gracenote M2any Audible Magic (Muscle Fish)

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Basic framework

(Cano et al. 2005)

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Challenges

VarianceCompressionDistortionNoise

EfficiencyEncodingLoopkup

Database sizeSearch algorithm

Music High dimensionality

GOALS

RobustCompact

Fast

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Extraction

Fingerprint extraction (Cano et al. 2005)

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Searching

Euclidean / HMM sequence Pre-computed distances Multi-staged searching (coarse to fine) Indexing Candidate pruning Table lookup

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Table lookup database (Haitsma et al. 2002)

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References

Cano, P., E. Batlle, T. Kalker, and J. Haitsma. 2005. A review of audio fingerprinting. Journal of VLSI Signal Processing Systems 41 (3): 271–84.

Haitsma, J., and T. Kalker. 2002. A highly robust audio fingerprinting system. Proceedings of the International Conference on Music Information Retrieval. 107–15.