Signal Processing Methods for Music TranscriptionAnssi Klapuri, Manuel Davy Springer Science & Business Media, 26/02/2007 - 440 páginas 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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... mixture signal into distinct sound sources and extracts the percep- tual parameters of these ( including pitch ) in some raw form . This is followed by two parallel modules which carry out pitch organization ( melodic contour analysis ...
... mixture of them . Percussion transcription both in the presence and absence of pitched instruments is discussed . Chapter 6 is concerned with the classification of pitched musical instru- ment sounds . This is useful for music ...
... mixture . This closely resembles the experience of an average listener who catches the melody or the ' theme ' of a piece of music even though he or she would not be able to distinguish the inner lines . Here , methods for extracting ...
... Mixture Models ( GMMs ) Gaussian mixture models are quite important in audio processing . For ex- ample in speech processing the data considered are generally sets of cepstral coefficients . However , this algorithm is not restricted to ...
... Mixture of five Gaussians . The five Gaussians composing the mixture are represented in dotted lines , whereas the full mixture pdf is represented in solid line ( with offset +1.1 for better visibility ) . The mixture coefficients are ...
Índice
17 | |
21 | |
Sparse Adaptive Representations for Musical Signals | 65 |
Stephen Hainsworth | 101 |
Unpitched Percussion Transcription | 130 |
Automatic Classification of Pitched Musical Instrument | 163 |
Multiple Fundamental Frequency Estimation Based | 203 |
Auditory ModelBased Methods for Multiple Fundamental | 228 |
Unsupervised Learning Methods for Source Separation | 267 |
Auditory Scene Analysis in Music Signals | 298 |
Music Scene Description | 327 |
Singing Transcription | 360 |
References | 391 |
Index | 429 |
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Signal Processing Methods for Music Transcription Anssi Klapuri,Manuel Davy Pré-visualização indisponível - 2010 |