Topic
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.
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21 Dec 1998TL;DR: In this article, a method and apparatus for coding a quasi-periodic speech signal is presented, which is represented by a residual signal generated by filtering the speech signal with a linear predictive coding (LPC) analysis filter.
Abstract: A method and apparatus for coding a quasi-periodic speech signal The speech signal is represented by a residual signal generated by filtering the speech signal with a Linear Predictive Coding (LPC) analysis filter The residual signal is encoded by extracting a prototype period from a current frame of the residual signal A first set of parameters is calculated which describes how to modify a previous prototype period to approximate the current prototype period One or more codevectors are selected which, when summed, approximate the error between the current prototype period and the modified previous prototype A multi-stage codebook is used to encode this error signal A second set of parameters describe these selected codevectors The decoder synthesizes an output speech signal by reconstructing a current prototype period based on the first and second set of parameters, and the previous reconstructed prototype period The residual signal is then interpolated over the region between the current and previous reconstructed prototype periods The decoder synthesizes output speech based on the interpolated residual signal
74 citations
TL;DR: A model of classification is proposed by the use of a discrete wavelet transform DWT to transform the signal and the GA and the classifier SVM algorithm is applied, which achieves the best accuracy.
73 citations
TL;DR: Object and subjective evaluations indicated that the proposed spectral envelope estimation algorithm can obtain a temporally stable spectral envelope and synthesize speech with higher sound quality than speech synthesized with other algorithms.
73 citations
TL;DR: In this paper, the autocorrelation LPC analysis of speech in additive noise is studied and the beneficial effects of proper preemphasis are reaffirmed in terms of decreased numerical error as well as decreased LPC order needed for a good spectral fit.
Abstract: A study of the autocorrelation LPC analysis of speech in additive noise is presented. In the noise-free case it is shown that finite word length implementation of the analysis may produce stable but poor spectral estimates. The beneficial effects of proper preemphasis are reaffirmed in terms of decreased numerical error as well as decreased LPC order needed for a good spectral fit. For the ease of noisy input speech the conditions for severe distortion of the spectral estimate are presented. A proper LPC spectral analysis of speech in additive noise is shown to require a higher order fit than currently used, a more precise implementation, and a more accurate parameter quantization for transmission.
73 citations
14 Apr 1991
TL;DR: Measurements were made of the correlation dimension of normally spoken speech from a single speaker, and the results reveal that most of the points in the state space of the signal lie very close to a manifold of a dimensionality of less than three, indicating that one should be able to construct a nonlinear predictor for speech that significantly outperforms linear predictors.
Abstract: Measurements were made of the correlation dimension of normally spoken speech from a single speaker, and the results reveal that most of the points in the state space of the signal lie very close to a manifold of a dimensionality of less than three. This result indicates that one should be able to construct a nonlinear predictor for speech that significantly outperforms linear predictors. To validate this conclusion, a nonparametric predictor was constructed which was able to produce a prediction gain approximately 3 dB better than an equivalent linear predictor. Similar improvements in signal-to-noise ratio were also observed when the nonlinear predictor was added to a simple speech coder. >
73 citations