An Introduction to Audio Content Analysis Applications in Signal Processing and Music Informatics

by Lerch, Alexander

• ISBN13: 9781118266823
• ISBN10: 111826682X
• Edition: 1st
• Format: Hardcover
• Copyright: 2012-08-14
• Publisher: Wiley-IEEE Press

Summary

With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included.

Author Biography

Alexander Lerch, PhD, is Managing Director and co-owner of zplane development, a research and development firm of licensable technology for digital audio signal processing for both the music software industry and major audio content distributors. Dr. Lerch also teaches audio content analysis at the Technical University of Berlin.

Table of Contents

Preface	p. xiii
Acronyms	p. xv
List of Symbols	p. xix
Introduction	p. 1
Audio Content	p. 3
A Generalized Audio Content Analysis System	p. 4
Fundamentals	p. 7
Audio Signals	p. 7
Periodic Signals	p. 7
Random Signals	p. 9
Sampling and Quantization	p. 9
Statistical Signal Description	p. 13
Signal Processing	p. 14
Convolution	p. 14
Block-Based Processing	p. 18
Fourier Transform	p. 20
Constant Q Transform	p. 23
Auditory Filterbanks	p. 24
Correlation Function	p. 24
Linear Prediction	p. 28
Instantaneous Features	p. 31
Audio Pre-Processing	p. 33
Down-Mixing	p. 33
DC Removal	p. 33
Normalization	p. 34
Down-Sampling	p. 34
Other Pre-Processing Options	p. 35
Statistical Properties	p. 35
Arithmetic Mean	p. 36
Geometric Mean	p. 36
Harmonic Mean	p. 36
Generalized Mean	p. 26
Centroid	p. 37
Variance and Standard Deviation	p. 37
Skewness	p. 38
Kurtosis	p. 39
Generalized Central Moments	p. 40
Quantiles and Quantile Ranges	p. 40
Spectral Shape	p. 41
Spectral Rolloff	p. 42
Spectral Flux	p. 44
Spectral Centroid	p. 45
Spectral Spread	p. 47
Spectral Decrease	p. 48
Spectral Slope	p. 49
Mel Frequency Cepstral Coefficients	p. 51
Signal Properties	p. 54
Tonalness	p. 54
Autocorrelation Coefficients	p. 61
Zero Crossing Rate	p. 62
Feature Post-Processing	p. 53
Derived Features	p. 64
Normalization and Mapping	p. 65
Subfeatures	p. 66
Feature Dimensionality Reduction	p. 66
Intensity	p. 71
Human Perception of Intensity and Loudness	p. 71
Representation of Dynamics in Music	p. 73
Features	p. 73
Root Mean Square	p. 73
Peak Envelope	p. 76
Psycho-Acoustic Loudness Features	p. 77
EBU R128	p. 78
Tonal Analysis	p. 79
Human Perception of Pitch	p. 79
Pitch Scales	p. 79
Chroma Perception	p. 81
Representation of Pitch in Music	p. 82
Pitch Classes and Names	p. 82
Intervals	p. 83
Root Note, Mode, and Key	p. 83
Chords and Harmony	p. 86
The Frequency of Musical Pitch	p. 88
Fundamental Frequency Detection	p. 91
Detection Accuracy	p. 92
Pre-Processing	p. 94
Monophonic Input Signals	p. 97
Polyphonic Input Signals	p. 103
Tuning Frequency Estimation	p. 106
Key Detection	p. 108
Pitch Chroma	p. 108
Key Recognition	p. 112
Chord Recognition	p. 116
Temporal Analysis	p. 119
Human Perception of Temporal Events	p. 119
Onsets	p. 119
Tempo and Meter	p. 122
Rhythm	p. 122
Timing	p. 123
Representation of Temporal Events in Music	p. 123
Tempo and Time Signature	p. 123
Note Value	p. 124
Onset Detection	p. 124
Novelty Function	p. 125
Peak Picking	p. 127
Evaluation	p. 128
Beat Histogram	p. 133
Beat Histogram Features	p. 134
Detection of Tempo and Beat Phase	p. 135
Detection of Meter and Downbeat	p. 136
Alignment	p. 139
Dynamic Time Warping	p. 139
Example	p. 143
Common Variants	p. 144
Optimizations	p. 145
Audio-to-Audio Alignment	p. 146
Ground Truth Data for Evaluation	p. 147
Audio-to-Score Alignment	p. 148
Real-Time Systems	p. 148
Non-Real-Time Systems	p. 149
Musical Genre, Similarity, and Mood	p. 151
Musical Genre Classification	p. 151
Musical Genre	p. 152
Feature Extraction	p. 154
Classification	p. 155
Related Research Fields	p. 156
Music Similarity Detection	p. 156
Mood Classification	p. 158
Instrument Recognition	p. 161
Audio Fingerprinting	p. 163
Fingerprint Extraction	p. 164
Fingerprint Matching	p. 165
Fingerprinting System: Example	p. 166
Music Performance Analysis	p. 169
Musical Communication	p. 169
Score	p. 169
Music Performance	p. 170
Production	p. 172
Recipient	p. 172
Music Performance Analysis	p. 172
Analysis Data	p. 173
Research Results	p. 177
Convolution Properties	p. 181
Identity	p. 181
Commutativity	p. 181
Associativity	p. 182
Distributivity	p. 183
Circularity	p. 183
Fourier Transform	p. 185
Properties of the Fourier Transformation	p. 186
Inverse Fourier Transform	p. 186
Superposition	p. 186
Convolution and Multiplication	p. 186
Parseval's Theorem	p. 187
Time and Frequency Shift	p. 188
Symmetry	p. 188
Time and Frequency Scaling	p. 189
Derivatives	p. 190
Spectrum of Example Time Domain Signals	p. 190
Delta Function	p. 190
Constant	p. 191
Cosine	p. 191
Rectangular Window	p. 191
Delta Pulse	p. 191
Transformation of Sampled Time Signals	p. 192
Short Time Fourier Transform of Continuous Signals	p. 192
Window Functions	p. 193
Discrete Fourier Transform	p. 195
Window Functions	p. 196
Fast Fourier Transform	p. 197
Principal Component Analysis	p. 199
Computation of the Transformation Matrix	p. 200
Interpretation of the Transformation Matrix	p. 200
Software for Audio Analysis	p. 201
Software Frameworks and Applications	p. 202
Marsyas	p. 202
CLAM	p. 202
jMIR	p. 203
CoMTRVA	p. 203
Sonic Visualiser	p. 203
Software Libraries and Toolboxes	p. 204
Feature Extraction	p. 204
Plugin Interfaces	p. 205
Other Software	p. 206
References	p. 207
Index	p. 243
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