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:
https://lup.lub.lu.se/record/1527291
- author
- Christensen, Mads
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2009
- 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
- 2016-04-01 12:38:41
- date last changed
- 2024-03-12 19:12:49
@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}},
publisher = {{Morgan & Claypool}},
series = {{Synthesis Lectures on Speech and Audio Processing}},
title = {{Multi-pitch estimation}},
url = {{http://dx.doi.org/10.2200/S00178ED1V01Y200903SAP005}},
doi = {{10.2200/S00178ED1V01Y200903SAP005}},
year = {{2009}},
}