Topic
Cepstrum
About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.
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TL;DR: The use of the Hartley transform (HT) in cepstrum analysis, as a substitute for the more commonly used Fourier transform (FT), is examined.
Abstract: The use of the Hartley transform (HT) in cepstrum analysis, as a substitute for the more commonly used Fourier transform (FT), is examined. With this substitution, the input to the cepstrum must be in the real domain only. The benefits of using the HT are approximately 50% less data memory required and approximately 40% faster program execution, at no loss in accuracy. >
17 citations
14 Apr 1991
TL;DR: A novel cepstral function, the cepstrum (CEP) of the one-sided autocorrelation sequence (COSA), is presented and applied to pitch determination of speech signals and significantly reduces their pitch-period errors at transitional speech segments as well as in speech signals contaminated by noise.
Abstract: A novel cepstral function, the cepstrum (CEP) of the one-sided autocorrelation sequence (COSA), is presented and applied to pitch determination of speech signals. This pitch determination algorithm (PDA) starts from the autocorrelation sequence in lieu of the speech signal. Although the COSA pitch determination algorithm does not improve the performance of the autocorrelation-with-center-clipping and CEP algorithms in quasiperiodic speech frames, it significantly reduces their pitch-period errors at transitional speech segments as well as in speech signals contaminated by noise. The PDA's better performance is based on its accuracy at nonstationary segments of speech signals and its noise capability. >
17 citations
31 Mar 2009
TL;DR: In this work, F-ratio is computed as a theoretical measure to validate the experimental results for both identification and verification of composite speaker identification/verification.
Abstract: The main objective of this paper is to explore the effectiveness of feature selection for performing composite speaker identification/verification. We propose features such as line spectral frequency (LSF), differential line spectral frequency (DLSF), mel frequency cepstral coefficients (MFCC), discrete cosine transform cepstrum (DCTC), perceptual linear predictive cepstrum (PLP) and mel frequency perceptual linear predictive cepstrum (MF-PLP). These features are captured and training models are developed by K-means clustering procedure. A speaker identification system is evaluated on noise added test speeches and the experimental results reveal the performance of the proposed algorithm in identifying speakers based on minimum distance between test features and clusters and also highlight the best choice of feature set among all the proposed features for 50 speakers chosen randomly from "TIMIT" database. Analysis is performed on the identification results to emphasize the choice of features which produce better results for speaker verification with respect to equal error rate. In this work, F-ratio is computed as a theoretical measure to validate the experimental results for both identification and verification.
17 citations
TL;DR: Both the real and the power pseudocepstra using discrete cosine transform and discrete sine transform are reported and they are applied to speech pitch period extraction.
Abstract: Some empirical results in use of discrete trigonometric transforms for cepstrum analysis are presented. Both the real and the power pseudocepstra using discrete cosine transform and discrete sine transform are reported. They are applied to speech pitch period extraction. Comparisons are made with standard Fourier transform cepstrum analysis. >
17 citations
TL;DR: This paper shows that the use of the cepstrum to determine the pitch of a signal does not work on periodic signals.
Abstract: In a paper by A. Michael Noll [J. Acoust. Soc. Am. 41, 293–309 (1967)], the use of the cepstrum was proposed to determine the pitch of a signal. This paper shows that such a method does not work on periodic signals.
17 citations