Extracting features from musical audio signal
Some References
This bibliography was compiled during 2004. It may not be up to date.
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Signal Processing
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Rafael A. Irizarry.
Local harmonic estimation in musical sound signals.
Journal of the American Statistical Association, 2001.
NOTE: A local harmonic model that tracks changes in pitch and in the
amplitudes of the harmonics is fit. Ways to choose appropriate window sizes
are also studied.
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Rafael A. Irizarry.
Weighted estimation of harmonic components in a musical sound signal.
Journal of Time Series Analysis, 23:29-48, 1999.
NOTE: A paper about the harmonic signal plus noise musical sound
model.
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Alain de Cheveigné.
Time domain processing in the auditory system.
In ICONIP (International Conference on Neural Information
Processing), pages 1327-1332, 1998.
NOTE: Review models of auditory signal processing that operate in the
time domain.
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Xavier Rodet.
Musical sound signal analysis/synthesis: Sinusoidal+residual
and elementary waveform models.
In Proceedings of the IEEE Time-Frequency and Time-Scale
Workshop (TFTS'97), University of Warwick, Coventry, UK, 27th-29th August
1997.
NOTE: Another paper about the harmonic plus noise sound model.
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Alan V. Oppenheim and Ronald W. Schafer.
Discrete-time signal processing.
Prentice Hall signal processing series. Prentice-Hall, 1989.
NOTE: The classical introduction to discrete-time signal processing.
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General Feature Extraction in Musical Audio Signal
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Alexander Sheh and Daniel P.W. Ellis.
Chord segmentation and recognition using em-trained hidden markov
models.
In 4th International Symposium on Music Information Retrieval
ISMIR-03, Baltimore, October 2003.
NOTE: System for automatic chord transcription using speech tools.
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Christopher J.C. Burges, John C. Platt, and Soumaya Jana.
Distorsion discriminant analysis for audio fingerprinting.
IEEE Transactions on Speech and Audio Processing,
11(3):165-174, 2003.
NOTE: This method constructs a linear, convolutional neural network
out of layers, each of which performs an oriented PCA.
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M. Davy and S. J. Godsill.
Bayesian harmonic models for musical signal analysis.
Bayesian Statistics, 7, 2003.
NOTE: Use of Bayesian structures in order to infer quantities about
musical signals at the highest level, such as pitch, dynamics, timbre,
instrument identity, etc.
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Eric D. Scheirer.
Music-Listening Systems.
PhD thesis, Massachusetts Institute of Technology (MIT), June 2000.
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Jonathan Foote.
A similarity measure for automatic audio classification.
In Proceedings of the AAAI 1997 Spring Symposium on Intelligent
Integration and Use of Text, Image, Video, and Audio Corpora, March 1997.
NOTE: Use of the MMI-supervised vector quantizer as a measure of
audio similarity.
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Music Information Retrieval
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F. Pachet and A. Zils.
Evolving automatically high-level music descriptors from acoustic
signals.
Springer Verlag LNCS, 2771, 2003.
NOTE: Genetic algorithms used to extract descriptors from audio
signals.
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Adam Berenzweig, Daniel P.W. Ellis, and Steve Lawrence.
Anchor space for classification and similarity measurement of music.
In IEEE International Conference on Multimedia and Expo, 2003.
NOTE: Mapping of music into a space where dimensions correspond to
``semantic'' features.
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Malcolm Slaney.
Mixtures of probability experts for audio retrieval and indexing.
In Proceedings of the IEEE International Conference on
Multimedia and Expo, Lausanne, Switzerland, August 2002.
NOTE: Two different mixture-of-probability-expert models are trained
to learn the association between acoustic queries and the corresponding
semantic explanation, and visa versa.
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Geoffroy Peeters and Xavier Rodet.
Automatically selecting signal descriptors for sound classification.
In ICMC 2002, Goteborg, Sweden, september 2002.
NOTE: Design of the CUIDADO classifier based on discriminant analysis
and mutual information.
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François Pachet, Gert Westermann, and Damien Laigre.
Musical data mining for electronic music distribution.
In Proceedings of the 1st WedelMusic Conference, 2001.
NOTE: Classical data-mining techniques such as co-occurence and
correlation analysis for classification of music titles.
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Musical Genre Recognition
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Patrick Zanon and Gerhard Widmer.
Recognition of famous pianists using machine learning algorithms:
First experimental results.
Technical Report OEFAI-TR-2003-01, ÖEFAI, 2003.
NOTE: A very interesting application of musical genre recognition.
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Jean-Julien Aucouturier and François Pachet.
Representing musical genre: A state of the art.
Journal of New Music Research, 32(1):83-93, 2003.
NOTE: Classify representations of musical genre in three categories:
manual, prescriptive and emergent approaches.
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George Tzanetakis and Perry Cook.
Musical genre classification of audio signals.
IEEE Transactions on Speech and Audio Processing, 10(5), July
2002.
NOTE: Results comparable to those obtain with humans are obtained
using different features as inputs to classifiers.
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George Tzanetakis, Georg Essl, and Perry Cook.
Automatic musical genre classification of audio signals.
In Proceedings of the International Symposium on Music
Information Retrieval (ISMIR), Bloomington, Indiana, 2001.
NOTE: Describing a set of features for representing texture,
instrumentation, rythmic structure and strength. Evaluation of these features
for classification purposes.
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François Pachet and Daniel Cazaly.
A taxonomy of musical genres.
In Proceedings of Content-Based Multimedia Information Access
(RIAO) Conference, Paris, France, 2000.
NOTE: Analysis of existing taxonomies of musical genre and
description of a new one.
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Hagen Soltau, Tanja Schultz, Martin Westphal, and Alex Waibel.
Recognition of music types.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing, ICASSP, 1998.
NOTE: Explicit Time Modelling with Neural Networks.
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Transcription
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Ali Taylan Cemgil, Bert Kappen, and David Barber.
A generative model for music transcription.
Submitted to IEEE Transactions on Speech and Audio Processing. NOTE:
Dynamical Bayesian Network for polyphonic music transcription, 2004.
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Ali Taylan Cemgil, Bert Kappen, and David Barber.
Generative model based polyphonic music transcription.
In Proc. of IEEE WASPAA, New Paltz, NY, October 2003. IEEE
Workshop on Applications of Signal Processing to Audio and Acoustics.
NOTE: Dynamical bayesian network for simultaneous tempo and
polyphonic pitch tracking.
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Anssi Klapuri.
Automatic transcription of music.
Master's thesis, Tampere University of Technology, 2001.
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Paul Joseph Walmsley.
Signal Separation of Musical Instruments.
PhD thesis, Department of Engineering, University of Cambridge,
September 2000.
NOTE: Simulation methods for musical signal decomposition and
transcription.
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Andrew D. Sterian.
Model-Based Segmentation of Time-Frequency Images for Musical
Transcription.
PhD thesis, University of Michigan, 1999.
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Keith D. Martin.
Automatic transcription of simple polyphonic music.
In Third Joint Meeting of the Acoustical Societies of America
and Japan, 1996.
NOTE: A transcription system based on the log-lag correlogram.
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Pitch Tracking
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Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, and Yann LeCun.
Real time voice processing with audiovisual feedback: toward
autonomous agents with perfect pitch.
In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in
Neural Information Processing Systems 15, pages 1205-1212, Cambridge, MA,
2003. MIT Press.
NOTE: Real time front end for detecting voiced speech and estimating
its fundamental frequency.
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Alain de Cheveigné and Hideki Kawahara.
Yin, a fundamental frequency estimator for speech and music.
J. Acoust. Soc. Am., 111:1917-1930, 2002.
NOTE: A fundamental frequency estimator based on autocorrelations.
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Lucas Parra and Uday Jain.
Approximate kalman filtering for the harmonic plus noise model.
In IEEE Workshop on Applications of Signal Processing to Audio
and Acoustics, pages 75-78, 2001.
NOTE: Probabilistic description of the harmonic plus noise model that
permits the development of a Kalman filter that tracks pitch.
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Anssi Klapuri, Tuomas Virtanen, and Jan-Markus Holm.
Robust multipitch estimation for the analysis and manipulation of
polyphonic musical signals.
In Proc. COST-G6 Conference on Digital Audio Effects, DAFx-00,
Verona, Italy, 2000.
NOTE: Pitch estimation on one time frame from chords comprising 1 to
6 distincts notes.
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Anssi Klapuri.
Pitch estimation using multiple independent time-frequency windows.
In Proc. IEEE Workshop on Applications of Signal Processing to
Audio and Acoustics, New Paltz, New York, Oct. 17-20 1999.
NOTE: Pitch model that calculates independent pitch estimates in
separate time-frequency windows and then combines them.
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Malcolm Slaney and Richard F. Lyon.
A perceptual pitch detector.
In Proceedings of the International Conference on Acoustics,
Speech and Signal Processing, volume 1, pages 357-360, 1990.
NOTE: Pitch detector based on Licklider's ``Duplex Theory'' of pitch
perception.
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Beat Tracking and Quantization
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George Tzanetakis, Georg Essl, and Perry Cook.
Human perception and computer extraction of beat strength.
In Proceedings of the Conference on Digital Audio Effects
(DAFX), Hamburg, Germany, September 2002.
Description of automatic beat strength measures.
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Masataka Goto.
An audio-based real-time beat tracking system for music with or
without drum-sounds.
Journal of New Music Research, 30(2):159-171, 2001.
NOTE: Uses onset times, chord changes, and drum patterns.
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Ali Taylan Cemgil, Peter Desain, and Bert Kappen.
Rhythm quantization for transcription.
Computer Music Journal, 24(2):60-76, 2000.
NOTE: Quantization of short groups of onsets using bayesian
statistics.
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Masataka Goto and Yoichi Muraoka.
An audio-based real-time beat tracking system and its applications.
In Proceedings of the 1998 International Computer Music
Conference, pages 17-20, October 1998.
NOTE: Real-time beat tracking system based on onset times, chord
changes and drum patterns.
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Eric D. Scheirer.
Tempo and beat analysis of acoustic musical signals.
J. Acoust. Soc. Am., 103(1):588-601, January 1998.
NOTE: This method uses a small number of bandpass filters and banks
of parallel comb filters to analyse the tempo of, and extract the beat from,
musical signals of arbitrary polyphonic complexity and containing arbitrary
timbres.
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Timbre Recognition
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Adam L. Berenzweig and Daniel P.W. Ellis.
Locating singing voice segments within music signals.
In IEEE Workshop on Apps. of Sig. Proc. to Acous. and Audio,
Mohonk, NY, October 2001.
NOTE: HMM trained over speech processing features for segmentation.
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Antti Eronen.
Automatic musical instrument recognition.
Master's thesis, Tampere University of Technology, 2001.
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Antti Eronen and Anssi Klapuri.
Musical instrument recognition using cepstral coefficients and
temporal features.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing, pages 753-756. ICASSP, 2000.
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Geoffroy Peeters, Stephen McAdams, and Perfecto Herrera.
Instrument sound description in the context of mpeg-7.
In ICMC 2000, Berlin, Germany, 2000.
NOTE: Description of instrument sounds based on perceptual features.
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Judith C. Brown.
Computer identification of musical instruments using pattern
recognition with cepstral coefficients as features.
J. Acoust. Soc. Am., 105:1933-1941, 1999.
NOTE: Classification of oboe and saxophone sounds. Performances
comparables to those obtained with humans.
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Auditory Scene Analysis
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André J. W. van der Kouwe, DeLiang Wang, and Guy J. Brown.
A comparison of auditory and blind separation techniques for speech
segregation.
IEEE Transactions on Speech and Audio Processing, 9:189-195,
2001.
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Sam T. Roweis.
One microphone source separation.
In Neural Information Processing Systems (NIPS) 13, pages
793-799, 2000.
NOTE: Learning of a nonstationary reweighting of frequency sub-bands
from a single recording in order to recover individual sound sources.
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Tuomas Virtanen and Anssi Klapuri.
Separation of harmonic sound sources using sinusoidal modeling.
In IEEE International Conference on Acoustics, Speech and Signal
Processing, ICASSP, 2000.
NOTE: First extract sinusoidal spectral components and then
calculates perceptual distances between sinusoidal trajectories in order to
separate multiple sound sources.
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Darryl Godsmark and Guy J. Brown.
A blackboard architecture for computational auditory scene analysis.
Speech Communication, 27:351-366, 1999.
NOTE: The model integrates evidence from multiple grouping principles
and manages competition between these principles in a manner that is
consistent with psychophysical findings.
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Guy J. Brown and Martin Cooke.
Computational auditory scene analysis.
Computer Speech and Language, 8:297-336, 1994.
NOTE: One of the first CASA systems (there is a problem with the pdf
file.).
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Real-Time Accompaniment
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Christopher Raphael.
Orchestral musical accompaniment from synthesized audio.
In ICMC 2003, 2003.
NOTE: Accompaniment system where audio output is synthesized by
playing back an audio recording at variable rate.
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Christopher Raphael.
A probabilistic expert system for automatic musical accompaniment.
Jour. of Comp. and Graph. Stats., 10(3):487-512, 2001.
NOTE: Bayesian Belief Networks used for musical accompaniment.
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Christopher Raphael.
A bayesian network for real-time musical accompaniment.
In Neural Information Processing Systems (NIPS), volume 14,
2001.
NOTE: Bayesian network representing the joint distribution on the
times at which the solo and accompaniment notes are played. Very interesting
work.
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Audio Fingerprinting
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Leandro de C.T. Gomes, Pedro Cano, Emilia Gómez, Madelein Bonnet, and Eloi
Battle.
Audio watermarking and fingerprinting: For which applications?
Journal of New Music Research, 32(1), 2003.
NOTE: Technical review and potential applications of both
methodologies.
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Emilia Gómez, Pedro Cano, Leandro de C. T. Gomes, Eloi Batlle, and Madelein
Bonnet.
Mixed watermarking-fingerprinting approach for integrity verification
of audio recordings.
In Proceedings of IEEE International Telecommunications
Symposium, Natal, Brazil, 2002.
NOTE: Method for audio-integrity verification based on a combination
of watermarking and fingerprinting.
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Symbolic Algorithms
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D.Conklin.
Music and Artificial Intelligence: Lecture Notes in Artificial
Intelligence, volume 2445, chapter Representation and Discovery of Vertical
Patterns in Music, pages 32-42.
Springer-Verlag, 2002.
NOTE: Method for discovering patterns in the vertical and horizontal
dimensions of polyphonic music.
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This bibliography was generated by Jean-François Paiement.