Signal Processing Methods for Music Transcription (Google eBook)

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Springer Science & Business Media, Feb 26, 2007 - Music - 452 pages
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Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index.
  

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Contents

72 Offline Approaches
210
73 OnLine Approaches
217
74 Other OnLine Bayesian Approaches
225
75 Conclusions
227
Auditory ModelBased Methods for Multiple Fundamental Frequency Estimation
228
82 Musical Sounds and FO Estimation
231
83 Pitch Perception Models
234
84 Using an Auditory Model as a Front End
244

Sparse Adaptive Representations for Musical Signals
65
32 Parametric Representations
68
33 Waveform Representations
70
34 Conclusion
97
Rhythm and Timbre Analysis
99
Beat Tracking and Musical Metre Analysis
101
42 Summary of BeatTracking Approaches
102
43 Musical Background to Rhythmic Structure
105
44 Onset Detection
107
45 RuleBased Approaches
111
46 Autocorrelation Methods
112
47 Oscillating Filter Approaches
113
48 Histogramming Methods
115
49 Multiple Agent Approaches
116
410 Probabilistic Models
117
411 Comparison of Algorithms
124
412 Conclusions
127
Unpitched Percussion Transcription
130
52 Pattern Recognition Approaches
133
53 SeparationBased Approaches
142
54 Musicological Modelling
153
55 Conclusions
160
56 Acknowledgements
162
Automatic Classification of Pitched Musical Instrument Sounds
163
62 Methodology
169
63 Features and Their Selection
171
64 Classification Techniques
184
65 Classification of Isolated Sounds
188
66 Classification of Sounds from Music Files
193
Multiple Fundamental Frequency Analysis
201
Multiple Fundamental Frequency Estimation Based on Generative Models
203
71 Noisy SumofSines Models
204
85 Computational Multiple FO Estimation Methods
248
86 Conclusions
264
Unsupervised Learning Methods for Source Separation in Monaural Music Signals
267
92 Signal Model
268
93 Independent Component Analysis
274
94 Sparse Coding
278
95 NonNegative Matrix Factorization
282
96 Prior Information about Sources
284
97 Further Processing of the Components
286
98 TimeVarying Components
289
99 Evaluation of the Separation Quality
294
910 Summary and Discussion
295
Entire Systems Acoustic and Musicological Modelling
297
Auditory Scene Analysis in Music Signals
298
102 Strategy for Music Scene Analysis
304
103 Probabilistic Models for Music Scene Analysis
313
From Grouping to Generative Estimation
324
Music Scene Description Masataka Goto
327
112 Estimating Melody and Bass Lines
330
113 Estimating Beat Structure
341
114 Estimating Drums
342
116 Evaluation Issues
355
118 Conclusion
358
Singing Transcription
360
122 Singing Signals
364
123 Feature Extraction
368
124 Converting Features into Note Sequences
375
125 Summary and Discussion
390
References
391
Index
429
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