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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
TL;DR: This paper deals with the problem of enhancing speech signals that have been degraded by statistically independent quasistationary noise and proposes maximum likelihood estimation solutions that are based upon the E–M algorithm and its derivatives.
Abstract: This paper deals with the problem of enhancing speech signals that have been degraded by statistically independent quasistationary noise. The estimation of the clean speech waveform, and of the parameters of autoregressive (AR) models for the clean speech, given the noisy speech, is considered. The two problems are demonstrated to be closely related in the sense that a good solution to one of them can be used for achieving a satisfactory solution for the other. The difficulties in solving these estimation problems are mainly due to the lack of explicit knowledge of the statistics of the clean speech signal and of the noise process. Maximum likelihood estimation solutions that are based upon the E–M algorithm and its derivatives are proposed. For estimating the speech waveform, the statistics of the clean speech signal and of the noise process are first estimated by training a pair of Gaussian AR hidden Markov models, one for the clean speech and the other for the noise, using long training sequences from the two sources. Then, the speech waveform is reestimated by applying the E–M algorithm to the estimated statistics. An approximation to the E–M algorithm is interpreted as being an iterative procedure in which Wiener filtering and AR modeling are alternatively applied. The different algorithms considered here will be compared and demonstrated.

115 citations

Proceedings ArticleDOI
14 Apr 1991
TL;DR: An efficient procedure for searching such a large codebook deploying a focused search strategy, where less than 0.1% of the codebook is searched with performance very close to that of a full search is described.
Abstract: The application of algebraic code excited linear prediction (ACELP) coding to wideband speech is presented An algebraic codebook with a 20 bit address can be used without any storage requirements and, more importantly, with a very efficient search procedure which allows for real-time implementation The authors describe an efficient procedure for searching such a large codebook deploying a focused search strategy, where less than 01% of the codebook is searched with performance very close to that of a full search High-quality speech at a bit rate of 13 kbps was obtained >

114 citations

Proceedings ArticleDOI
B. Atal1, M. Schroeder
01 Apr 1980
TL;DR: This method of quantization not only improves the speech quality by accurate quantization of the predicted residual when its amplitude is large but also allows encoding of the prediction residual at bit rates below 1 bit/sample.
Abstract: Adaptive predictive coding of speech signals at bit rates lower than 10 kbits/sec often requires the use of 2-level (1 bit) quantization of the samples of the prediction residual. Such a coarse quantization of the prediction residual can produce audible quantizing noise in the reproduced speech signal at the receiver. This paper describes a new method of quantization for improving the speech quality. The improvement is obtained by center clipping the prediction residual and by fine quantization of the high-amplitude portions of the prediction residual. The threshold of center clipping is adjusted to provide encoding of the prediction residual at a specified bit rate. This method of quantization not only improves the speech quality by accurate quantization of the prediction residual when its amplitude is large but also allows encoding of the prediction residual at bit rates below 1 bit/sample.

113 citations

Journal ArticleDOI
TL;DR: In this paper, classification of various human activities based on micro-Doppler signatures is studied using linear predictive coding (LPC) to reduce the computational time cost for extracting features, which makes real-time processing feasible.
Abstract: In this letter, classification of various human activities based on micro-Doppler signatures is studied using linear predictive coding (LPC). LPC is proposed to extract the features of micro-Doppler that are mixtures of different frequencies. The use of LPC can not only decrease the time frame required to capture the Doppler signature of human motion but can also reduce the computational time cost for extracting its features, which makes real-time processing feasible. The measured data of 12 human subjects performing seven different activities using a Doppler radar are used. These activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. A support vector machine is then trained using the output of LPC to classify the activities. Multiclass classification is implemented using a one-versus-one decision structure. The resulting classification accuracy is found to be over 85%. The effects of the number of LPC coefficients and the size of the sliding time window, as well as the decision time-frame size used in the extraction of micro-Doppler signatures, are also discussed.

113 citations


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