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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07 May 2001
TL;DR: A new algorithm is proposed for generating synthetic frequency components in the high-band given the low-band ones for wide-band speech synthesis based on linear prediction (LPC) analysis-synthesis based on spectral envelope extension and bandwidth extension of the LPC analysis residual using a spectral folding.
Abstract: This paper contributes to narrowband speech enhancement by means of frequency bandwidth extension A new algorithm is proposed for generating synthetic frequency components in the high-band (ie, 4-8 kHz) given the low-band ones (ie, 0-4 kHz) for wide-band speech synthesis It is based on linear prediction (LPC) analysis-synthesis It consists of a spectral envelope extension using efficiently line spectral frequencies (LSF) and a bandwidth extension of the LPC analysis residual using a spectral folding The low-band LSF of the synthesis signal are obtained from the input speech signal and the high-band LSF are estimated from the low-band ones using statistical models This estimation is achieved by means of four models that are distinguished by means of the first two reflection coefficients obtained from the input signal linear prediction analysis
106 citations
TL;DR: The source filter model of speech production is adopted as presented in X. Huang et al. (2001), wherein speech is divided into two broad classes: voiced and unvoiced.
Abstract: In this article, we concentrate on spectral estimation techniques that are useful in extracting the features to be used by automatic speech recognition (ASR) system. As an aid to understanding the spectral estimation process for speech signals, we adopt the source filter model of speech production as presented in X. Huang et al. (2001), wherein speech is divided into two broad classes: voiced and unvoiced. Voiced speech is quasi-periodic, consisting of a fundamental frequency corresponding to the pitch of a speaker, as well as its harmonics. Unvoiced speech is stochastic in nature and is best modeled as white noise convolved with an infinite impulse response filter.
105 citations
18 Mar 2005
TL;DR: This paper presents a hybrid audio coding algorithm integrating an LP-based coding technique and a more general transform coding technique, which has consistently high performance for both speech and music signals.
Abstract: This paper presents a hybrid audio coding algorithm integrating an LP-based coding technique and a more general transform coding technique. ACELP is used in LP-based coding mode, whereas algebraic TCX is used in transform coding mode. The algorithm extends previously published work on ACELP/TCX coding in several ways. The frame length is increased to 80 ms, adaptive multi-length sub-frames are used with overlapping windowing, an extended multi-rate algebraic VQ is applied to the TCX spectrum to avoid quantizer saturation, and noise shaping is improved. Results show that the proposed hybrid coder has consistently high performance for both speech and music signals.
105 citations
TL;DR: A spectral domain, speech enhancement algorithm based on a mixture model for the short time spectrum of the clean speech signal, and on a maximum assumption in the production of the noisy speech spectrum that shows improved performance compared to alternative speech enhancement algorithms.
Abstract: We present a spectral domain, speech enhancement algorithm. The new algorithm is based on a mixture model for the short time spectrum of the clean speech signal, and on a maximum assumption in the production of the noisy speech spectrum. In the past this model was used in the context of noise robust speech recognition. In this paper we show that this model is also effective for improving the quality of speech signals corrupted by additive noise. The computational requirements of the algorithm can be significantly reduced, essentially without paying performance penalties, by incorporating a dual codebook scheme with tied variances. Experiments, using recorded speech signals and actual noise sources, show that in spite of its low computational requirements, the algorithm shows improved performance compared to alternative speech enhancement algorithms.
104 citations
TL;DR: The distortion performance of the vector quantization approach for LPC voice coding is examined both analytically and experimentally to show its relationship with the residual minimization process in LPC analysis.
Abstract: The distortion performance of the vector quantization approach for LPC voice coding is examined both analytically and experimentally. Analytically, interpretations of the interparameter coupling effects of a distortion measure and the clustering nature of the algorithm for LPC vector quantization are obtained to show its relationship with the residual minimization process in LPC analysis. Experimentally, a large database of speech is used to compare its performance and properties to scalar quantization. The results lend further insight into the superior performance of vector quantization.
104 citations