Signal Processing Methods for Music Transcription

By: Anssi Klapuri (Editor), Manuel Davy (Editor)

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Hardcover | 1 May 2006

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456 Pages

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24.13 x 16.51 x 2.54

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This text is the first published survey of recent research in signal processing for music transcription, edited and authored by authorities in the field. It covers a range of topics, from the structure and decomposition of signals, to using computational modeling and neural networks for music transcription. The book targets a growing audience interested in MPEG-7 standardization. It serves as an ideal starting point for newcomers and an excellent reference source for people already working in the field. The text is enhanced by a common reference and index.

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Published: 29th October 2010

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Preface	p. ix
List of Contributors	p. xi
Foundations
Introduction to Music Transcription	p. 3
Terminology and Concepts	p. 7
Perspectives on Music Transcription	p. 11
Outline	p. 17
An Introduction to Statistical Signal Processing and Spectrum Estimation	p. 21
Frequency, Time-Frequency, and Cepstral Representations	p. 21
Basic Statistical Methods	p. 28
Bayesian Statistical Methods	p. 39
Pattern Recognition Methods	p. 52
Sparse Adaptive Representations for Musical Signals	p. 65
Introduction	p. 65
Parametric Representations	p. 68
Waveform Representations	p. 70
Conclusion	p. 97
Rhythm and Timbre Analysis
Beat Tracking and Musical Metre Analysis	p. 101
Introduction	p. 101
Summary of Beat-Tracking Approaches	p. 102
Musical Background to Rhythmic Structure	p. 105
Onset Detection	p. 107
Rule-Based Approaches	p. 111
Autocorrelation Methods	p. 112
Oscillating Filter Approaches	p. 113
Histogramming Methods	p. 115
Multiple Agent Approaches	p. 116
Probabilistic Models	p. 117
Comparison of Algorithms	p. 124
Conclusions	p. 127
Unpitched Percussion Transcription	p. 131
Introduction	p. 131
Pattern Recognition Approaches	p. 133
Separation-Based Approaches	p. 142
Musicological Modelling	p. 153
Conclusions	p. 160
Acknowledgements	p. 162
Automatic Classification of Pitched Musical Instrument Sounds	p. 163
Introduction	p. 163
Methodology	p. 169
Features and Their Selection	p. 171
Classification Techniques	p. 184
Classification of Isolated Sounds	p. 188
Classification of Sounds from Music Files	p. 193
Conclusions	p. 199
Multiple Fundamental Frequency Analysis
Multiple Fundamental Frequency Estimation Based on Generative Models	p. 203
Noisy Sum-of-Sines Models	p. 204
Off-line Approaches	p. 210
On-Line Approaches	p. 217
Other On-Line Bayesian Approaches	p. 225
Conclusions	p. 227
Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation	p. 229
Introduction	p. 229
Musical Sounds and F0 Estimation	p. 231
Pitch Perception Models	p. 234
Using an Auditory Model as a Front End	p. 244
Computational Multiple F0 Estimation Methods	p. 248
Conclusions	p. 264
Unsupervised Learning Methods for Source Separation in Monaural Music Signals	p. 267
Introduction	p. 267
Signal Model	p. 268
Independent Component Analysis	p. 274
Sparse Coding	p. 278
Non-Negative Matrix Factorization	p. 282
Prior Information about Sources	p. 284
Further Processing of the Components	p. 286
Time-Varying Components	p. 289
Evaluation of the Separation Quality	p. 294
Summary and Discussion	p. 295
Entire Systems, Acoustic and Musicological Modelling
Auditory Scene Analysis in Music Signals	p. 299
Introduction	p. 299
Strategy for Music Scene Analysis	p. 304
Probabilistic Models for Music Scene Analysis	p. 313
Conclusion: From Grouping to Generative Estimation	p. 324
Music Scene Description	p. 327
Introduction	p. 327
Estimating Melody and Bass Lines	p. 330
Estimating Beat Structure	p. 341
Estimating Drums	p. 342
Estimating Chorus Sections and Repeated Sections	p. 342
Evaluation Issues	p. 355
Applications of Music Scene Description	p. 355
Conclusion	p. 358
Singing Transcription	p. 361
Introduction	p. 361
Singing Signals	p. 364
Feature Extraction	p. 368
Converting Features into Note Sequences	p. 375
Summary and Discussion	p. 390
References	p. 391
Index	p. 429
Table of Contents provided by Ingram. All Rights Reserved.

Signal Processing Methods for Music Transcription

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