A robust and computationally efficient subspace-based fundamental frequency estimator
IEEE Transactions on Audio, Speech, and Language Processing
Optimal filters for extraction and separation of periodic sources
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle
IEEE Transactions on Audio, Speech, and Language Processing
Optimal filter designs for separating and enhancing periodic signals
IEEE Transactions on Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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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. Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude Estimation