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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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Patent
Toshiyuki Morii1
27 Nov 1995
TL;DR: In this article, a speech is analyzed by a speech analyzing unit to obtain sample characteristic parameters, and a coding distortion is calculated from the sampled characteristic parameters in each of a plurality of coding modules.
Abstract: A sample speech is analyzed by a speech analyzing unit to obtain sample characteristic parameters, and a coding distortion is calculated from the sample characteristic parameters in each of a plurality of coding modules. The sample characteristic parameters and the coding distortions are statistically processed by a statistical processing unit to obtain a coding module selecting rule. Thereafter, when a speech is analyzed by the speech analyzing unit to obtain characteristic parameters, an appropriate coding module is selected by a coding module selecting unit from the coding modules according to the coding module selecting rule on condition that a coding distortion for the characteristic parameters is minimized in the appropriate coding module. Thereafter, the characteristic parameters of the speech are coded in the appropriate coding module, and a coded speech is obtained. When the coded speech is decoded, a reproduced speech is obtained. Accordingly, because an appropriate coding module can be easily selected from a plurality of coding modules according to the coding module selecting rule, any allophone occurring in a reproduced speech can be prevented at a low calculation volume.

75 citations

Journal ArticleDOI
TL;DR: This paper investigates the problem of ordering the color table such that the absolute sum of prediction errors is minimized and gives two heuristic solutions for the problem that can be achieved over dictionary-based coding schemes that are commonly employed for color-mapped images.
Abstract: Linear predictive techniques perform poorly when used with color-mapped images where pixel values represent indices that point to color values in a look-up table. Reordering the color table, however, can lead to a lower entropy of prediction errors. In this paper, we investigate the problem of ordering the color table such that the absolute sum of prediction errors is minimized. The problem turns out to be intractable, even for the simple case of one-dimensional (1-D) prediction schemes. We give two heuristic solutions for the problem and use them for ordering the color table prior to encoding the image by lossless predictive techniques. We demonstrate that significant improvements in actual bit rates can be achieved over dictionary-based coding schemes that are commonly employed for color-mapped images.

74 citations

Journal ArticleDOI
TL;DR: The equivalence of linear prediction and AR spectral estimation is exploited to show theoretically, and with simulations, thatAR spectral estimation from subbands offers a gain over fullband AR spectral estimating.
Abstract: Linear prediction schemes make a prediction x/spl circ//sub i/ of a data sample x/sub i/ using p previous samples. It has been shown by Woods and O'Neil (1986) as well as Pearlman (1991) that as the order of prediction p/spl rarr//spl infin/, there is no gain to be obtained by coding subband samples. This paper deals with the less well understood theory of finite-order prediction and optimal coding from subbands which are generated by ideal (brickwall) filtering of a stationary Gaussian source. We first prove that pth-order prediction from subbands is superior to pth-order prediction in the fullband, when p is finite. This fact adduces that optimal vector p-tuple coding in the subbands is shown to offer quantifiable gains over optimal fullband p-tuple coding, again when p is finite. The properties of subband spectra are analyzed using the spectral flatness measure. These results are used to prove that subband DPCM provides a coding gain over fullband DPCM, for finite orders of prediction. In addition, the proofs provide means of quantifying the subband advantages in linear prediction, optimal coding, and DPCM coding in the form of gain formulas. Subband decomposition of a source is shown to result in a whitening of the composite subband spectrum. This implies that, for any stationary source, a pth-order prediction error filter (PEF) can be found that is better than the pth-order PEF obtained by solving the Yule-Walker equations resulting from the fullband data. We demonstrate the existence of such a "super-optimal" PEF and provide algorithmic approaches to obtaining this PEF. The equivalence of linear prediction and AR spectral estimation is then exploited to show theoretically, and with simulations, that AR spectral estimation from subbands offers a gain over fullband AR spectral estimation.

74 citations

Journal ArticleDOI
TL;DR: The STI can be computed using speech probe waveforms and the values of the resulting indices are as good predictors of intelligibility scores as those derived from MTFs by theoretical methods.
Abstract: A method for computing the speech transmission index (STI) using real speech stimuli is presented and evaluated. The method reduces the effects of some of the artifacts that can be encountered when speech waveforms are used as probe stimuli. Speech-based STIs are computed for conversational and clearly articulated speech in several noisy, reverberant, and noisy-reverberant environments and compared with speech intelligibility scores. The results indicate that, for each speaking style, the speech-based STI values are monotonically related to intelligibility scores for the degraded speech conditions tested. Therefore, the STI can be computed using speech probe waveforms and the values of the resulting indices are as good predictors of intelligibility scores as those derived from MTFs by theoretical methods.

74 citations

Book
24 Sep 2007
TL;DR: This book discusses Speech Signals and Wavelets and Pitch Detection, Predictive Coding, and the Quadratic Spline Wavelets, and concludes with a comparison of Speech Transceivers and their applications.
Abstract: About the Authors. Other Wiley and IEEE Press Books on Related Topics. Preface and Motivation. Acknowledgements. I Speech Signals andWaveform Coding. 2 Predictive Coding. 3 Analysis-by-synthesis Principles. 4 Speech Spectral Quantization. 5 RPE Coding. 6 Forward-Adaptive CELP Coding. 7 Standard CELP Codecs. 8 Backward-Adaptive CELP Coding. 9 Wideband Speech Coding. 10 MPEG-4 Audio Compression and Transmission. 11 Overview of Low-rate Speech Coding. 12 Linear Predictive Vocoder. 13 Wavelets and Pitch Detection. 14 Zinc Function Excitation. 15 Mixed-Multiband Excitation. 16 Sinusoidal Transform Coding Below 4kbps. 17 Conclusions on Low Rate Coding. 18 Comparison of Speech Transceivers. 19 Voice Over the Internet Protocol. A Constructing the Quadratic Spline Wavelets. B Zinc Function Excitation. C Probability Density Function for Amplitudes. Bibliography. Index. Author Index.

74 citations


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