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Multi-pitch estimation

Christensen, Mads and Jakobsson, Andreas LU (2009) In Synthesis Lectures on Speech and Audio Processing
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Book/Report
publication status
published
subject
in
Synthesis Lectures on Speech and Audio Processing
pages
160 pages
publisher
Morgan & Claypool
external identifiers
  • scopus:61849084604
ISSN
1932-1678
1932-121X
DOI
10.2200/S00178ED1V01Y200903SAP005
language
English
LU publication?
yes
id
94c43924-5986-4271-bd01-8b6be81db2e4 (old id 1527291)
date added to LUP
2010-01-11 11:53:53
date last changed
2017-10-01 03:59:20
@book{94c43924-5986-4271-bd01-8b6be81db2e4,
  abstract     = {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.},
  author       = {Christensen, Mads and Jakobsson, Andreas},
  issn         = {1932-1678},
  language     = {eng},
  pages        = {160},
  publisher    = {Morgan & Claypool},
  series       = {Synthesis Lectures on Speech and Audio Processing},
  title        = {Multi-pitch estimation},
  url          = {http://dx.doi.org/10.2200/S00178ED1V01Y200903SAP005},
  year         = {2009},
}