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Linear predictive coding

About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.


Papers
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Journal ArticleDOI
Chong Un1, Hyeong Gi Lee1
TL;DR: This paper presents a new method of voiced/unvoiced/ silence discrimination of speech based on the results of counting bit alternations of the bit stream from linear delta modulation of the speech signal and zero crossings of a band-pass filtered output of the decoded LDM signal.
Abstract: This paper presents a new method of voiced/unvoiced/ silence discrimination of speech. The decision algorithm is based on the results of counting bit alternations of the bit stream from linear delta modulation (LDM) of the speech signal and zero crossings of a band-pass filtered output of the decoded LDM signal. Computer simulation of the system with real speech has yielded accurate results. Economical realization of the discriminator hardware using standard integrated circuits is also considered.

26 citations

Patent
16 Nov 1998
TL;DR: In this article, a method of pitch estimation which utilizes perception based analysis by synthesis for improved pitch estimation over a variety of input speech conditions is presented, where pitch candidates are generated corresponding to a plurality of sub-ranges within a pitch search range and a residual spectrum is determined for a segment of speech and a reference speech signal is generated from the residual spectrum using sinusoidal synthesis and linear predictive coding (LPC) synthesis.
Abstract: The present invention provides a method of pitch estimation which utilizes perception based analysis by synthesis for improved pitch estimation over a variety of input speech conditions. Initially, pitch candidates are generated corresponding to a plurality of sub-ranges within a pitch search range (item 2). Then a residual spectrum is determined for a segment of speech (item 4) and a reference speech signal is generated from the residual spectrum using sinusoidal synthesis (item 8) and linear predictive coding (LPC) synthesis (item 9). A synthetic speech signal is generated for each of the pitch candidates using sinusoidal (item 12) and LPC synthesis (item 13). Finally, the synthetic speech signal for each pitch candidate is compared with the reference residual signal (item 14) to determine an optimal pitch estimate based on a pitch period of a synthetic speech signal that provides a maximum signal to noise ratio.

26 citations

Journal ArticleDOI
TL;DR: The results indicate that the hybrid use of articulatory, perceptual and prosodic features of speech, combined with a supervised dimensionality-reduction procedure, is able to outperform any individual acoustic model for speech-driven facial animation.

26 citations

Proceedings ArticleDOI
25 Mar 2012
TL;DR: This contribution presents a new consistent solution for MMSE speech amplitude (SA) estimation under SPU, being based on the generalized gamma distribution representing a variety of speech priors, and is shown to outperform both the SPU-based MMSE-SA estimator relying on a Gaussian speech prior, and the gamma MM SE-SA estimation without SPU.
Abstract: Several investigations showed that speech enhancement approaches can be improved by speech presence uncertainty (SPU) estimation. Although there has been a strong focus on the use of correct statistical models for spectral weighting rules for the last few decades, there is just a few publications about SPU estimation based on a speech prior consistent with the spectral weighting rule. This contribution presents a new consistent solution for MMSE speech amplitude (SA) estimation under SPU, being based on the generalized gamma distribution representing a variety of speech priors. Employing the gamma speech model which is a special case of the generalized gamma distribution, the new approach is shown to outperform both the SPU-based MMSE-SA estimator relying on a Gaussian speech prior, and the gamma MMSE-SA estimation without SPU.

26 citations

Proceedings ArticleDOI
K. Oh1, C. Un
19 Mar 1984
TL;DR: It has been found that for pitch detection of noisy speech the algorithm that uses an AMDF or an autocorrelation function yields relatively good performance than others.
Abstract: Results of a performance comparison study of eight pitch extraction algorithms for noisy as well as clean speech are presented. These algorithms are the autocorrelation method with center clipping, the autocorrelation method with modified center clipping, the simplified inverse filter tracking (SIFT) method, the average magnitude difference function (AMDF) method, the pitch detection method based on LPC inverse filtering and AMDF, the data reduction method, the parallel processing method and the cepstrum method. It has been found that for pitch detection of noisy speech the algorithm that uses an AMDF or an autocorrelation function yields relatively good performance than others. A pitch detector that uses center clipped speech as an input signal is effective in pitch extraction of noisy speech. In general, preprocessing such as LPC inverse filtering or center clipping of input speech yields remarkable improvement in pitch detection.

26 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202225
202126
202042
201925
201837