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: In this article, a method and apparatus for detecting speech activity in an input signal is presented, which includes performing begin point detection using power/zero crossing. Once the begin point has been detected, the present invention uses the cepstrum of the input signal to determine the endpoint of the sound in the signal.
Abstract: A method and apparatus for detecting speech activity in an input signal. The present invention includes performing begin point detection using power/zero crossing. Once the begin point has been detected, the present invention uses the cepstrum of the input signal to determine the endpoint of the sound in the signal. After both the beginning and ending of the sound are detected, the present invention uses vector quantization distortion to classify the sound as speech or noise.
31 citations
TL;DR: Experiments are presented suggesting that the combination of the SNR-cepstrum with the well known perceptual linear prediction method can be beneficial in noisy environments.
31 citations
TL;DR: A new speech feature extracted from adaptive wavelet for speech recognition is described, which shows a slightly better recognition rate than the cepstrum for speaker independent speech recognition and shows a lower standard deviation between speakers than does the cEPstrum.
Abstract: A new speech feature extracted from adaptive wavelet for speech recognition is described. The speech signal is decomposed through adapted local trigonometric transforms. The decomposed signal is classified by M uniform sub-bands for each subinterval. The energy of each sub-band is used as a speech feature. This feature is applied to vector quantisation and the hidden Markov model. The new speech feature shows a slightly better recognition rate than the cepstrum for speaker independent speech recognition. The new speech feature also shows a lower standard deviation between speakers than does the cepstrum.
31 citations
Patent•
02 Dec 2008TL;DR: In this article, a method and apparatus for vibration-based automatic condition monitoring of a wind turbine, comprising the steps of determining a set of vibration measurement values of the wind turbine; calculating a frequency spectrum of the set of vibrations; selecting at least one quefrency in the frequency spectrum; and detecting an alarm condition based upon an amplitude of the cepstrum at the selected queferency, and a wind turbines therefor.
Abstract: Method and apparatus for vibration-based automatic condition monitoring of a wind turbine, comprising the steps of: determining a set of vibration measurement values of the wind turbine; calculating a frequency spectrum of the set of vibration measurement values; calculating a cepstrum of the frequency spectrum; selecting at least one quefrency in the cepstrum, and detecting an alarm condition based upon an amplitude of the cepstrum at the selected quefrency, and a wind turbine therefor.
31 citations
01 Jan 2007
TL;DR: It is concluded that the advantages do not in fact stem from auditory properties, and that there is so far little or no evidence that the study of the human auditory system has contributed to advances in automatic speech recognition.
Abstract: The paper begins by discussing the difficulties in obtaining repeatable results in speech recognition. Theoretical arguments are presented for and against copying human auditory properties in automatic speech recognition. The “standard” acoustic analysis for automatic speech recognition, consisting of melscale cepstrum coefficients and their temporal derivatives, is described. Some variations and extensions of the standard analysis — PLP, cepstrum correlation methods, LDA, and variants on log power — are then discussed. These techniques pass the test of having been found useful at multiple sites, especially with noisy speech. The extent to which auditory properties can account for the advantage found for particular techniques is considered. It is concluded that the advantages do not in fact stem from auditory properties, and that there is so far little or no evidence that the study of the human auditory system has contributed to advances in automatic speech recognition. Contributions in the future are not, however, ruled out.
31 citations