Multi-pitch estimation

• 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: Speech processing systems > Mathematical models.; Estimation theory > Mathematical models.; Intonation (Phonetics) > Measurement > Mathematical models.
• Online Access: Abstract with links to full text
• Item Description: Part of: Synthesis digital library of engineering and computer science.; Title from PDF t.p. (viewed on March 9, 2009).; Series from website.
• Physical Description: 1 electronic text (xi, 142 p. : ill.) : digital file.; Also available in print.
• Format: Mode of access: World Wide Web.; System requirements: Adobe Acrobat reader.
• Bibliography: Includes bibliographical references (p. 125-138) and index.
• ISBN: 9781598298390 (electronic bk.); 9781598298383 (pbk.)
• ISSN: 1932-1678
• Access: Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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.