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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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Proceedings ArticleDOI
23 Mar 1992
TL;DR: Preliminary tests conducted using only the linear prediction coding (LPC) cepstrum as features have shown that the use of HSMM increased the phoneme recognition accuracy to 53.7% from the 48.4% obtained using an HMM.
Abstract: Hidden Markov models (HMMs) have been used to model speech in many areas of speech processing. One characteristic of the HMM is that the probability of time spent in a particular state, or state occupancy, is geometrically distributed. This, however, becomes a serious limitation and results in inaccurate modeling when the HMMs are used for phoneme recognition. The authors use hidden semi-Markov models (HSMM) to overcome the above limitation. Semi-Markov models are a more general class of Markov chains in which the state occupancy can be explicitly modeled by an arbitrary probability mass distribution. The authors use non-parametric distributions to describe the state occupancies instead of parametric distributions such as gamma. Poisson or binomial, as analysis of actual data shows that the duration of some phonemes could not be approximated by any of the above. Preliminary tests conducted using only the linear prediction coding (LPC) cepstrum as features have shown that the use of HSMM increased the phoneme recognition accuracy to 53.7% from the 48.4% obtained using an HMM. >

12 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed feature set not only display a high recognition rate and excellent anti-noise performance in speech recognition, but can also fully characterize the auditory and energy information in the speech signals.
Abstract: Environmental noise can pose a threat to the stable operation of current speech recognition systems. It is therefore essential to develop a front feature set that is able to identify speech under low signal-to-noise ratio. In this paper, a robust fusion feature is proposed that can fully characterize speech information. To obtain the cochlear filter cepstral coefficients (CFCC), a novel feature is first extracted by the power-law nonlinear function, which can simulate the auditory characteristics of the human ear. Speech enhancement technology is then introduced into the front end of feature extraction, and the extracted feature and their first-order difference are combined in new mixed features. An energy feature Teager energy operator cepstral coefficient (TEOCC) is also extracted, and combined with the above-mentioned mixed features to form the fusion feature sets. Principal component analysis (PCA) is then applied to feature selection and optimization of the feature set, and the final feature set is used in a non-specific persons, isolated words, and small-vocabulary speech recognition system. Finally, a comparative experiment of speech recognition is designed to verify the advantages of the proposed feature set using a support vector machine (SVM). The experimental results show that the proposed feature set not only display a high recognition rate and excellent anti-noise performance in speech recognition, but can also fully characterize the auditory and energy information in the speech signals.

12 citations

Journal ArticleDOI
TL;DR: A new adaptive blind equalization method, the Power Cepstrum and Tricoherence Equalization Algorithm (POTEA), based on second- and fourth-order statistics of the received sequence, which performs simultaneous identification and equalization of a nonminimum phase channel from its output only.
Abstract: This paper introduces a new adaptive blind equalization method, the Power Cepstrum and Tricoherence Equalization Algorithm (POTEA), based on second- and fourth-order statistics of the received sequence. The algorithm performs simultaneous identification and equalization of a nonminimum phase channel from its output only. Simulation results, with QAM signals, are presented to demonstrate the effectiveness of POTEA.

12 citations

Proceedings ArticleDOI
15 Jul 2013
TL;DR: A novel onset detection algorithm based on cepstral analysis that achieves significant improvement in performance over other algorithms, particularly for pitched instruments with soft onsets, such as violin and singing voice.
Abstract: This paper presents a novel onset detection algorithm based on cepstral analysis. Instead of considering unnecessary mel-scale or any interests of non-harmonic components, we selectively focus on the changes in particular cepstral coefficients that represent the harmonic structure of an input signal. In comparison with a conventional time-frequency analysis, the advantage of using cepstral coefficients is that it shows the harmonic structure more clearly, and gives a robust detection function even when the envelope of waveform fluctuates or slowly increases. As a detection function, harmonic cepstrum regularity (HCR) is derived by the summation of several harmonic cepstral coefficients, but their frequency indices are defined from the previous frame so as to reflect the temporal changes in the harmonic structure. Experiments show that the proposed algorithm achieves significant improvement in performance over other algorithms, particularly for pitched instruments with soft onsets, such as violin and singing voice.

12 citations

Proceedings ArticleDOI
14 Apr 1991
TL;DR: A least-squares method is presented to estimate the cross-bicepstral parameters of the three signals simultaneously, assuming only that the signals are exponential, stable, and have no zeros on the unit circle.
Abstract: Complex cepstrum techniques are applied to cross-bispectra in order to simultaneously reconstruct three independent finite-time signals, given the cross-bispectrum of the three signals only. The method is applied to simultaneously identify three systems via the cross-bispectrum of their outputs, knowing only the statistics of the input. A least-squares method is presented to estimate the cross-bicepstral parameters of the three signals simultaneously. The method is nonparametric and noniterative, assuming only that the signals (or impulse responses) are exponential, stable, and have no zeros on the unit circle. It takes full advantage of the two-dimensional nature of the bispectrum to reconstruct the three signals (or identify the three systems) simultaneously. >

11 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202386
2022206
202160
202096
2019135
2018130