Multi-pitch estimation

Periodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitc...

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Bibliographic Details
Main Author: Christensen, Mads Græsbøll.
Other Authors: Jakobsson, Andreas.
Format: Electronic
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2009.
Series:Synthesis lectures on speech and audio processing (Online) ; # 5.
Subjects:
Online Access:Abstract with links to full text
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020 # # |a 9781598298383 (pbk.) 
024 7 # |a 10.2200/S000178ED1V01Y200903SAP005  |2 doi 
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100 1 # |a Christensen, Mads Græsbøll. 
245 1 0 |a Multi-pitch estimation  |c Mads Græsbøll Christensen, Andreas Jakobsson.  |h [electronic resource] / 
260 # # |a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :  |b Morgan & Claypool Publishers,  |c c2009. 
300 # # |a 1 electronic text (xi, 142 p. : ill.) :  |b digital file. 
490 1 # |a Synthesis lectures on speech & audio processing,  |v # 5  |x 1932-1678 ; 
500 # # |a Part of: Synthesis digital library of engineering and computer science. 
500 # # |a Title from PDF t.p. (viewed on March 9, 2009). 
500 # # |a Series from website. 
504 # # |a Includes bibliographical references (p. 125-138) and index. 
505 0 # |a Fundamentals -- Introduction -- Related work -- Some applications -- Signal models -- Covariance matrix model -- Speech and audio signals -- Other signal models -- Parameter estimation bounds -- Evaluation of pitch estimators -- Statistical methods -- Introduction -- Maximum likelihood estimation -- Noise covariance matrix estimation -- White noise case -- Some maximum a posteriori estimators -- MAP model and order selection -- Fast multi-pitch estimation -- Expectation maximization -- Another related method -- Harmonic fitting -- Some results -- Discussion -- Filtering methods -- Introduction -- Comb filtering -- Filterbank interpretation of NLS -- Optimal filterbank design -- Optimal filter design -- Asymptotic analysis -- Inverse covariance matrix -- Variance and order estimation -- Fast implementation -- Some results -- Discussion -- Subspace methods -- Introduction -- Signal and noise subspace identification -- Subspace properties -- Pre-whitening -- Rank estimation using Eigenvalues -- Angles between subspaces -- Estimation using orthogonality -- Robust estimation -- Estimation using shift-invariance -- Some results -- Discussion -- Amplitude estimation -- Introduction -- Least squares estimation -- Capon- and APES-like amplitude estimates -- Some results and discussion -- The analytic signal -- Bibliography -- About the authors. 
506 # # |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. 
510 0 # |a Compendex 
510 0 # |a INSPEC 
510 0 # |a Google scholar 
510 0 # |a Google book search 
520 # # |a Periodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented.The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness. 
530 # # |a Also available in print. 
538 # # |a Mode of access: World Wide Web. 
538 # # |a System requirements: Adobe Acrobat reader. 
650 # 0 |a Speech processing systems  |x Mathematical models. 
650 # 0 |a Estimation theory  |x Mathematical models. 
650 # 0 |a Intonation (Phonetics)  |x Measurement  |x Mathematical models. 
690 # # |a Estimation theory 
690 # # |a Spectral estimation 
690 # # |a Pitch estimation 
690 # # |a Pitch detection 
690 # # |a Fundamental frequency estimation 
690 # # |a Statistical signal processing 
690 # # |a Order estimation 
690 # # |a Model selection 
690 # # |a Audio processing 
690 # # |a Audio analysis 
690 # # |a Music transcription 
690 # # |a Speech processing 
690 # # |a Comb filtering 
690 # # |a Subspace methods 
690 # # |a Optimal filtering 
700 1 # |a Jakobsson, Andreas. 
730 0 # |a Synthesis digital library of engineering and computer science. 
830 # 0 |a Synthesis lectures on speech and audio processing (Online) ;  |v # 5. 
856 4 2 |u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.2200/S00178ED1V01Y200903SAP005  |3 Abstract with links to full text