scispace - formally typeset
Search or ask a question
Topic

Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a merit index is introduced that allows the automatic selection of the intrinsic mode functions that should be used for the calculation of the Hilbert-Huang spectrum of a spiral bevel gearbox.

184 citations

Proceedings ArticleDOI
07 May 2001
TL;DR: In this paper, the authors present several mechanisms that enable effective spread-spectrum audio watermarking systems: prevention against detection desynchronization, cepstrum filtering, and chess watermarks.
Abstract: We present several mechanisms that enable effective spread-spectrum audio watermarking systems: prevention against detection desynchronization, cepstrum filtering, and chess watermarks. We have incorporated these techniques into a system capable of reliably detecting a watermark in an audio clip that has been modified using a composition of attacks that degrade the original audio characteristics well beyond the limit of acceptable quality. Such attacks include: fluctuating scaling in the time and frequency domain, compression, addition and multiplication of noise, resampling, requantization, normalization, filtering, and random cutting and pasting of signal samples.

182 citations

Journal ArticleDOI
TL;DR: The group delay function is modified to overcome the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects and is called the modified group delay feature (MODGDF).
Abstract: Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed

181 citations

Journal ArticleDOI
TL;DR: A digital speech analysis‐synthesis system based on a recently proposed approach to the deconvolution of speech is presented and either a zero‐phase or minimum‐phase characteristic can be obtained by simple weighting of the cepstrum before transformation.
Abstract: A digital speech analysis‐synthesis system based on a recently proposed approach to the deconvolution of speech is presented. The analyzer is based on a computation of the cepstrum considered as the inverse Fourier transform of the log magnitude of the Fourier transform. The transmitted parameters represent pitch and voiced unvoiced information and the low‐time portion of the cepstrum representing an approximation to the cepstrum of the vocal‐tract impulse response. In the synthesis, the low‐time cepstral information is transformed to an impulse response function, which is then convolved with a train of impulses during voiced portions or a noise waveform during unvoiced portions to reconstruct the speech. Since no phase information is retained in the analysis, phase must be regenerated during synthesis. Either a zero‐phase or minimum‐phase characteristic can be obtained by simple weighting of the cepstrum before transformation.

178 citations

Journal ArticleDOI
TL;DR: Simulation results indicate that the proposed bispectrum-based approach performs significantly better than the classical power spectrum based approach at low signal-to-noise ratios.
Abstract: The authors propose inspecting the zero crossings in the central slice of the bispectrum of the observed image for blur identification. This method is an extension of the classical methods for blur identification in which the power spectrum (or the power cepstrum) of the blurred image is applied to the bispectrum domain. The proposed bispectrum-based approach utilizes the ability of the bispectrum to suppress additive, signal-independent, Gaussian observation noise. Simulation results indicate that the method performs significantly better than the classical power spectrum based approach at low signal-to-noise ratios. >

177 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
82% related
Robustness (computer science)
94.7K papers, 1.6M citations
80% related
Feature (computer vision)
128.2K papers, 1.7M citations
79% related
Deep learning
79.8K papers, 2.1M citations
79% related
Support vector machine
73.6K papers, 1.7M citations
78% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202386
2022206
202160
202096
2019135
2018130