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Pitch Determination Of Speech Signals


Pitch Determination Of Speech Signals
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Pitch Determination Of Speech Signals


Pitch Determination Of Speech Signals
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Author : W. Hess
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Pitch Determination Of Speech Signals written by W. Hess and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.


Pitch (i.e., fundamental frequency FO and fundamental period TO) occupies a key position in the acoustic speech signal. The prosodic information of an utterance is predominantly determined by this parameter. The ear is more sensitive to changes of fundamental frequency than to changes of other speech signal parameters by an order of magnitude. The quality of vocoded speech is essentially influenced by the quality and faultlessness of the pitch measure ment. Hence the importance of this parameter necessitates using good and reliable measurement methods. At first glance the task looks simple: one just has to detect the funda mental frequency or period of a quasi-periodic signal. For a number of reasons, however, the task of pitch determination has to be counted among the most difficult problems in speech analysis. 1) In principle, speech is a nonstationary process; the momentary position of the vocal tract may change abruptly at any time. This leads to drastic variations in the temporal structure of the signal, even between subsequent pitch periods, and assuming a quasi-periodic signal is often far from realistic. 2) Due to the flexibility of the human vocal tract and the wide variety of voices, there exist a multitude of possible temporal structures. Narrow-band formants at low harmonics (especially at the second or third harmonic) are an additional source of difficulty. 3) For an arbitrary speech signal uttered by an unknown speaker, the fundamental frequency can vary over a range of almost four octaves (50 to 800 Hz).



Basic Techniques In Pitch Determination Of Speech Signals


Basic Techniques In Pitch Determination Of Speech Signals
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Author : Nghia Van Le
language : en
Publisher:
Release Date : 1991

Basic Techniques In Pitch Determination Of Speech Signals written by Nghia Van Le and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.




Pitch Determination Of Speech Signals


Pitch Determination Of Speech Signals
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Author : Mark David Anderson
language : en
Publisher:
Release Date : 1986

Pitch Determination Of Speech Signals written by Mark David Anderson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.




Pitch Determination Of Speech Signals In The Presence Of Noise


Pitch Determination Of Speech Signals In The Presence Of Noise
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Author : Martin Roy Varley
language : en
Publisher:
Release Date : 1990

Pitch Determination Of Speech Signals In The Presence Of Noise written by Martin Roy Varley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Image processing categories.




Pitch Determination Of Speech Signals Using The Generalized Spectrum


Pitch Determination Of Speech Signals Using The Generalized Spectrum
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Author : Tim Black
language : en
Publisher:
Release Date : 2000

Pitch Determination Of Speech Signals Using The Generalized Spectrum written by Tim Black and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.




Multi Pitch Estimation


Multi Pitch Estimation
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Author : Mads Græsbøll Christensen
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009

Multi Pitch Estimation written by Mads Græsbøll Christensen and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Audio frequency categories.


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



Visual Representations Of Speech Signals


Visual Representations Of Speech Signals
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Author : Martin Cooke
language : en
Publisher:
Release Date : 1993-04-14

Visual Representations Of Speech Signals written by Martin Cooke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-04-14 with Computers categories.


Presents a wide range of graphical representations of some speech signals and allows current speech analysis techniques to be assessed and directly compared. Describes time-frequency representations, auditory modeling, neural networks, pitch and multi-channel analysis. The study of over 40 different analyses of speech is represented in myriad images found throughout.



Pitch Estimation Of Speech Signals Using Cyclic Statistics


Pitch Estimation Of Speech Signals Using Cyclic Statistics
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Author : Osman Burak Onal
language : en
Publisher:
Release Date : 1997

Pitch Estimation Of Speech Signals Using Cyclic Statistics written by Osman Burak Onal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.




Digital Processing Of Speech Signals


Digital Processing Of Speech Signals
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Author : Lawrence R. Rabiner
language : en
Publisher: Prentice Hall
Release Date : 1978

Digital Processing Of Speech Signals written by Lawrence R. Rabiner and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Computers categories.


The material in this book is intended as a one-semester course in speech processing. The purpose of this text is to show how digital signal processing techniques can be applied to problems related to speech communication. The book gives an extensive description of the physical basis for speech coding including fourier analysis, digital representation and digital and time domain models of the wave form. It goes on to discuss homomorphic speech processing, linear predictive coding and digital processing for machine communication by voice.



New Time Frequency Domain Pitch Estimation Methods For Speed Signals Under Low Levels Of Snr


New Time Frequency Domain Pitch Estimation Methods For Speed Signals Under Low Levels Of Snr
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Author : Celia Shahnaz
language : en
Publisher:
Release Date : 2009

New Time Frequency Domain Pitch Estimation Methods For Speed Signals Under Low Levels Of Snr written by Celia Shahnaz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.


The major objective of this research is to develop novel pitch estimation methods capable of handling speech signals in practical situations where only noise-corrupted speech observations are available. With this objective in mind, the estimation task is carried out in two different approaches. In the first approach, the noisy speech observations are directly employed to develop two new time-frequency domain pitch estimation methods. These methods are based on extracting a pitch-harmonic and finding the corresponding harmonic number required for pitch estimation. Considering that voiced speech is the output of a vocal tract system driven by a sequence of pulses separated by the pitch period, in the second approach, instead of using the noisy speech directly for pitch estimation, an excitation-like signal (ELS) is first generated from the noisy speech or its noise- reduced version. In the first approach, at first, a harmonic cosine autocorrelation (HCAC) model of clean speech in terms of its pitch-harmonics is introduced. In order to extract a pitch-harmonic, we propose an optimization technique based on least-squares fitting of the autocorrelation function (ACF) of the noisy speech to the HCAC model. By exploiting the extracted pitch-harmonic along with the fast Fourier transform (FFT) based power spectrum of noisy speech, we then deduce a harmonic measure and a harmonic-to-noise-power ratio (HNPR) to determine the desired harmonic number of the extracted pitch-harmonic. In the proposed optimization, an initial estimate of the pitch-harmonic is obtained from the maximum peak of the smoothed FFT power spectrum. In addition to the HCAC model, where the cross-product terms of different harmonics are neglected, we derive a compact yet accurate harmonic sinusoidal autocorrelation (HSAC) model for clean speech signal. The new HSAC model is then used in the least-squares model-fitting optimization technique to extract a pitch-harmonic. In the second approach, first, we develop a pitch estimation method by using an excitation-like signal (ELS) generated from the noisy speech. To this end, a technique is based on the principle of homomorphic deconvolution is proposed for extracting the vocal-tract system (VTS) parameters from the noisy speech, which are utilized to perform an inverse-filtering of the noisy speech to produce a residual signal (RS). In order to reduce the effect of noise on the RS, a noise-compensation scheme is introduced in the autocorrelation domain. The noise-compensated ACF of the RS is then employed to generate a squared Hilbert envelope (SHE) as the ELS of the voiced speech. With a view to further overcome the adverse effect of noise on the ELS, a new symmetric normalized magnitude difference function of the ELS is proposed for eventual pitch estimation. Cepstrum has been widely used in speech signal processing but has limited capability of handling noise. One potential solution could be the introduction of a noise reduction block prior to pitch estimation based on the conventional cepstrum, a framework already available in many practical applications, such as mobile communication and hearing aids. Motivated by the advantages of the existing framework and considering the superiority of our ELS to the speech itself in providing clues for pitch information, we develop a cepstrum-based pitch estimation method by using the ELS obtained from the noise-reduced speech. For this purpose, we propose a noise subtraction scheme in frequency domain, which takes into account the possible cross-correlation between speech and noise and has advantages of noise being updated with time and adjusted at each frame. The enhanced speech thus obtained is utilized to extract the vocal-tract system (VTS) parameters via the homomorphic deconvolution technique. A residual signal (RS) is then produced by inverse-filtering the enhanced speech with the extracted VTS parameters. It is found that, unlike the previous ELS-based method, the squared Hilbert envelope (SHE) computed from the RS of the enhanced speech without noise compensation, is sufficient to represent an ELS. Finally, in order to tackle the undesirable effect of noise of the ELS at a very low SNR and overcome the limitation of the conventional cepstrum in handling different types of noises, a time-frequency domain pseudo cepstrum of the ELS of the enhanced speech, incorporating information of both magnitude and phase spectra of the ELS, is proposed for pitch estimation. (Abstract shortened by UMI.).