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Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics [Kõva köide]

4.29/5 (13 hinnangut Goodreads-ist)
(Technical University of Berlin, Germany)
  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 265x187x21 mm, kaal: 653 g
  • Ilmumisaeg: 09-Oct-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 111826682X
  • ISBN-13: 9781118266823
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Introduction to Audio Content Analysis: Applications in Signal Processing and Music InformaticsSuurem pilt
  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 265x187x21 mm, kaal: 653 g
  • Ilmumisaeg: 09-Oct-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 111826682X
  • ISBN-13: 9781118266823
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118266823.html
Märksõnad:
"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"--



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.

Please visit the companion website: www.AudioContentAnalysis.org

Arvustused

The book is simply a treasure for music analysts, and I would strongly recommend it for any scientific library.  (Computing Reviews, 29 May 2013)

Overall, this is a very practical book. Its a good source of concise information on many topics in audio analysis, and I recommend it for practitioners of digital audio.  (Computing Reviews, 4 January 2013)





 

Preface xiii
Acronyms xv
List of Symbols xix
1 Introduction 1(6)
1.1 Audio Content
3(1)
1.2 A Generalized Audio Content Analysis System
4(3)
2 Fundamentals 7(24)
2.1 Audio Signals
7(7)
2.1.1 Periodic Signals
7(2)
2.1.2 Random Signals
9(1)
2.1.3 Sampling and Quantization
9(4)
2.1.4 Statistical Signal Description
13(1)
2.2 Signal Processing
14(17)
2.2.1 Convolution
14(4)
2.2.2 Block-Based Processing
18(2)
2.2.3 Fourier Transform
20(3)
2.2.4 Constant Q Transform
23(1)
2.2.5 Auditory Filterbanks
24(1)
2.2.6 Correlation Function
24(4)
2.2.7 Linear Prediction
28(3)
3 Instantaneous Features 31(40)
3.1 Audio Pre-Processing
33(2)
3.1.1 Down-Mixing
33(1)
3.1.2 DC Removal
33(1)
3.1.3 Normalization
34(1)
3.1.4 Down-Sampling
34(1)
3.1.5 Other Pre-Processing Options
35(1)
3.2 Statistical Properties
35(6)
3.2.1 Arithmetic Mean
36(1)
3.2.2 Geometric Mean
36(1)
3.2.3 Harmonic Mean
36(1)
3.2.4 Generalized Mean
36(1)
3.2.5 Centroid
37(1)
3.2.6 Variance and Standard Deviation
37(1)
3.2.7 Skewness
38(1)
3.2.8 Kurtosis
39(1)
3.2.9 Generalized Central Moments
40(1)
3.2.10 Quantiles and Quantile Ranges
40(1)
3.3 Spectral Shape
41(13)
3.3.1 Spectral Rolloff
42(2)
3.3.2 Spectral Flux
44(1)
3.3.3 Spectral Centroid
45(2)
3.3.4 Spectral Spread
47(1)
3.3.5 Spectral Decrease
48(1)
3.3.6 Spectral Slope
49(2)
3.3.7 Mel Frequency Cepstral Coefficients
51(3)
3.4 Signal Properties
54(9)
3.4.1 Tonalness
54(7)
3.4.2 Autocorrelation Coefficients
61(1)
3.4.3 Zero Crossing Rate
62(1)
3.5 Feature Post-Processing
63(8)
3.5.1 Derived Features
64(1)
3.5.2 Normalization and Mapping
65(1)
3.5.3 Subfeatures
66(1)
3.5.4 Feature Dimensionality Reduction
66(5)
4 Intensity 71(8)
4.1 Human Perception of Intensity and Loudness
71(2)
4.2 Representation of Dynamics in Music
73(1)
4.3 Features
73(3)
4.3.1 Root Mean Square
73(3)
4.4 Peak Envelope
76(1)
4.5 Psycho-Acoustic Loudness Features
77(2)
4.5.1 EBU R128
78(1)
5 Tonal Analysis 79(40)
5.1 Human Perception of Pitch
79(3)
5.1.1 Pitch Scales
79(2)
5.1.2 Chroma Perception
81(1)
5.2 Representation of Pitch in Music
82(9)
5.2.1 Pitch Classes and Names
82(1)
5.2.2 Intervals
83(1)
5.2.3 Root Note, Mode, and Key
83(3)
5.2.4 Chords and Harmony
86(2)
5.2.5 The Frequency of Musical Pitch
88(3)
5.3 Fundamental Frequency Detection
91(15)
5.3.1 Detection Accuracy
92(2)
5.3.2 Pre-Processing
94(3)
5.3.3 Monophonic Input Signals
97(6)
5.3.4 Polyphonic Input Signals
103(3)
5.4 Tuning Frequency Estimation
106(2)
5.5 Key Detection
108(8)
5.5.1 Pitch Chroma
108(4)
5.5.2 Key Recognition
112(4)
5.6 Chord Recognition
116(3)
6 Temporal Analysis 119(20)
6.1 Human Perception of Temporal Events
119(4)
6.1.1 Onsets
119(3)
6.1.2 Tempo and Meter
122(1)
6.1.3 Rhythm
122(1)
6.1.4 Timing
123(1)
6.2 Representation of Temporal Events in Music
123(1)
6.2.1 Tempo and Time Signature
123(1)
6.2.2 Note Value
124(1)
6.3 Onset Detection
124(9)
6.3.1 Novelty Function
125(2)
6.3.2 Peak Picking
127(1)
6.3.3 Evaluation
128(5)
6.4 Beat Histogram
133(2)
6.4.1 Beat Histogram Features
134(1)
6.5 Detection of Tempo and Beat Phase
135(1)
6.6 Detection of Meter and Downbeat
136(3)
7 Alignment 139(12)
7.1 Dynamic Time Warping
139(7)
7.1.1 Example
143(1)
7.1.2 Common Variants
144(1)
7.1.3 Optimizations
145(1)
7.2 Audio-to-Audio Alignment
146(2)
7.2.1 Ground Truth Data for Evaluation
147(1)
7.3 Audio-to-Score Alignment
148(3)
7.3.1 Real-Time Systems M
148(1)
7.3.2 Non-Real-Time Systems
149(2)
8 Musical Genre, Similarity, and Mood 151(12)
8.1 Musical Genre Classification
151(5)
8.1.1 Musical Genre
152(2)
8.1.2 Feature Extraction
154(1)
8.1.3 Classification
155(1)
8.2 Related Research Fields
156(7)
8.2.1 Music Similarity Detection
156(2)
8.2.2 Mood Classification
158(3)
8.2.3 Instrument Recognition
161(2)
9 Audio Fingerprinting 163(6)
9.1 Fingerprint Extraction
164(1)
9.2 Fingerprint Matching
165(1)
9.3 Fingerprinting System: Example
166(3)
10 Music Performance Analysis 169(12)
10.1 Musical Communication
169(3)
10.1.1 Score
169(1)
10.1.2 Music Performance
170(2)
10.1.3 Production
172(1)
10.1.4 Recipient
172(1)
10.2 Music Performance Analysis
172(9)
10.2.1 Analysis Data
173(4)
10.2.2 Research Results
177(4)
A Convolution Properties 181(4)
A.1 Identity
181(1)
A.2 Commutativity
181(1)
A.3 Associativity
182(1)
A.4 Distributivity
183(1)
A.5 Circularity
183(2)
B Fourier Transform 185(14)
B.1 Properties of the Fourier Transformation
186(4)
B.1.1 Inverse Fourier Transform
186(1)
B.1.2 Superposition
186(1)
B.1.3 Convolution and Multiplication
186(1)
B.1.4 Parseval's Theorem
187(1)
B.1.5 Time and Frequency Shift
188(1)
B.1.6 Symmetry
188(1)
B.1.7 Time and Frequency Scaling
189(1)
B.1.8 Derivatives
190(1)
B.2 Spectrum of Example Time Domain Signals
190(2)
B.2.1 Delta Function
190(1)
B.2.2 Constant
191(1)
B.2.3 Cosine
191(1)
B.2.4 Rectangular Window
191(1)
B.2.5 Delta Pulse
191(1)
B.3 Transformation of Sampled Time Signals
192(1)
B.4 Short Time Fourier Transform of Continuous Signals
192(3)
B.4.1 Window Functions
193(2)
B.5 Discrete Fourier Transform
195(4)
B.5.1 Window Functions
196(1)
B.5.2 Fast Fourier Transform
197(2)
C Principal Component Analysis 199(2)
C.1 Computation of the Transformation Matrix
200(1)
C.2 Interpretation of the Transformation Matrix
200(1)
D Software for Audio Analysis 201(6)
D.1 Software Frameworks and Applications
202(2)
D.1.1 Marsyas
202(1)
D.1.2 CLAM
202(1)
D.1.3 jMIR
203(1)
D.1.4 CoMIRVA
203(1)
D.1.5 Sonic Visualiser
203(1)
D.2 Software Libraries and Toolboxes
204(3)
D.2.1 Feature Extraction
204(1)
D.2.2 Plugin Interfaces
205(1)
D.2.3 Other Software
206(1)
References 207(36)
Index 243
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.