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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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Proceedings ArticleDOI
17 May 2004
TL;DR: The paper describes the basic elements of the codec, points out envisaged applications, and gives an outline of the standardization process.
Abstract: Lossless coding is to become the latest extension of the MPEG-4 audio standard. In response to a call for proposals, many companies have submitted lossless audio codecs for evaluation. The codec of the Technical University of Berlin was chosen as reference model for MPEG-4 audio lossless coding (ALS), attaining working draft status in July 2003. The encoder is based on linear prediction, which enables high compression even with moderate complexity, while the corresponding decoder is straightforward. The paper describes the basic elements of the codec, points out envisaged applications, and gives an outline of the standardization process.

29 citations

PatentDOI
Motoaki Koyama1
TL;DR: In this paper, a speech segment detector is used to detect speech segments and a reference pattern memory for storing reference patterns, and a speech recognition section for comparing the detected speech segment detected by the detector with the reference patterns stored in the Reference Pattern Memory and selecting the reference pattern most similar to that of the speech segment.
Abstract: A speech recognition LSI system comprises a speech segment detector for detecting a speech segment from a speech segment detected, a reference pattern memory for storing reference patterns, and a speech recognition section for comparing the speech segment detected by the detector with the reference patterns stored in the reference pattern memory and selecting the reference pattern most similar to that of the speech segment. The system further comprises a recording/reproduction device for recording the speech signal and for reproducing only the speech segment the speech segment detector has detected, so that an operator can hear the speech segment.

29 citations

Proceedings ArticleDOI
29 Mar 1999
TL;DR: This investigation is directed to determine the effectiveness of an algorithm that uses multiple adaptive linear predictors, locally optimized on a pixel-by-pixel basis, and the results obtained are encouraging, where the algorithm improves over CALIC on some images.
Abstract: The practical lossless digital image compressors that achieve the best results in terms of compression ratio are also simple and fast algorithms with low complexity both in terms of memory usage and running time. Surprisingly, the compression ratio achieved by these systems cannot be substantially improved even by using image-by-image optimization techniques or more sophisticate and complex algorithms. Meyer and Tischer (1998) were able, with their TMW, to improve some current best results (they do not report results for all test images) by using global optimization techniques and multiple blended linear predictors. Our investigation is directed to determine the effectiveness of an algorithm that uses multiple adaptive linear predictors, locally optimized on a pixel-by-pixel basis. The results we obtained on a test set of nine standard images are encouraging, where we improve over CALIC on some images.

29 citations

Patent
23 Mar 2006
TL;DR: In this article, a system and method for improving the quality and intelligibility of speech signals is proposed, which applies frequency compression to the higher frequency components of speech signal while leaving lower frequency components substantially unchanged.
Abstract: A system and method are provided for improving the quality and intelligibility of speech signals. The system and method apply frequency compression to the higher frequency components of speech signals while leaving lower frequency components substantially unchanged. This preserves higher frequency information related to consonants which is typically lost to filtering and bandpass constraints. This information is preserved without significantly altering the fundamental pitch of the speech signal so that when the speech signal is reproduced its overall tone qualities are preserved. The system and method further apply frequency expansion to speech signals. Like the compression, only the upper frequencies of a received speech signal are expanded. When the frequency expansion is applied to a speech signal that has been compressed according to the invention, the speech signal is substantially returned to its pre-compressed state. However, frequency compression according to the invention provides improved intelligibility even when the speech signal is not subsequently re-expanded. Likewise, speech signals may be expanded even though the original signal was not compressed, without significant degradation of the speech signal quality. Thus, a transmitter may include the system for applying high frequency compression without regard to whether a receiver will be capable of re-expanding the signal. Likewise, a receiver may expand a received speech signal without regard to whether the signal was previously compressed.

29 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: A speech recognition system has been developed using different feature extraction techniques like MFCC (mel frequency cepestral coefficient), LPC (linear predictive coding) and HMM (hidden markov model) is used as the classifier and shows that MFCC performs well in each and every condition.
Abstract: To utilize the robot's capabilities, it is necessary for us to communicate with them efficiently Thus, Human Robot Interaction is attracting the attention of most of the researchers these days In this paper a speech recognition system has been developed using different feature extraction techniques like MFCC (mel frequency cepestral coefficient), LPC (linear predictive coding) and HMM (hidden markov model) is used as the classifier Less work has been done for Hindi language in this field with a vocabulary size not very large So, work in this paper has been done for Hindi database, with a vocabulary size a bit extended HMM has been implemented using HTK Toolkit Afterwards the performances of both of the techniques used have been compared The work has been done using audacity for sound recordings and Cygwin to execute the HTK commands in Linux type environment in windows platform As well as, the system developed has been tested in the speaker dependent and speaker independent both types of environments, whose performance results, as well as, the comparison graph of the system shows that MFCC performs well as compared to LPC in each and every condition

29 citations


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