Code-excited linear prediction

About: Code-excited linear prediction is a research topic. Over the lifetime, 2025 publications have been published within this topic receiving 28633 citations. The topic is also known as: CELP.

Analysis of Fundamental Frequency Contour of Coded Speech Based on Multi-Pulse Based Code Excited Linear Prediction Algorithm

Abstract: Problem statement: In low-bit-rate speech communication, speech coding deteriorates the characteristics of the coded speech significantly. An important feature of the speech is the fundamental frequency contour which determines the pitch information of the speech. It has been known that pitch information is one of the core parameter of the multi-pulse based code excited linear prediction (MPCELP) speech coder. Therefore the study of the deteriorated fundamental frequency contour should be conducted properly. Approach: This study proposes an analysis of the fundamental frequency contour of the coded speech based on MP-CELP speech coder. The comparison of the fundamental frequency contour of the natural speech and that of the coded speech has been performed. The MP-CELP with three levels of bitrate scalability is selected as the core speech coder. The speech material includes a hundred of male speech utterances and a hundred of female speech utterances. Results: The experimental results show that the speech coder causes the deterioration of the fundamental frequency contour empirically. The Root Mean Square Error (RMSE) between the fundamental frequency contour of the natural speech and that of the coded speech for three different bitrates has been conducted. The lower bitrate causes the higher value of RMSE. Conclusion: From the study, it is a proved that the MP-CELP speech coder deteriorates the fundamental frequency contour of the transmitted speech.

Classified vector excitation speech coding

Abstract: Code excited linear prediction (CELP) has been shown [1] to be capable of achieving high quality speech compression at very low bit rates. However, the original method suffers from the two drawbacks that it requires a large amount of computation, and the Gaussian codebook used is not able to reproduce a sharp impulse in the coded residual waveform. The authors present a classified vector excitation coding (CVXC) scheme based on selective modelling of the LPC residual which attempts to overcome both these drawbacks by using separate codebooks for separate excitation types. A further feature of our coder which helps preserve the nature of pulsive residuals, is the use of pairwise nearest neighbour (PNN) matching [2] to generate the excitation codebooks.

Classified conditional entropy coding of LSP parameters

Abstract: Summary form only given. A new LSP speech parameter compression scheme is proposed which uses conditional probability information through classification. For efficient compression of speech LSP parameter vectors it is essential that higher order correlations are exploited. The use of conditional probability information has been hindered by high complexity of the information. For example, a LSP vector has 34 bit representation at 4.8 K bps CELP coding (FS1016 standard). It is impractical to use the first order probability information directly since 2/sup 34//spl ap/1.7/spl times/10/sup 10/ number of probability tables would be required and training of such information would be practically impossible. In order to reduce the complexity, we reduce the input alphabet size by classifying the LSP vectors according to their phonetic relevance. In other words, speech LSP parameters are classified into groups representing loosely defined various phonemes. The number of phoneme groups used was 32 considering the ambiguity of similar phonemes and background noises. Then conditional probability tables are constructed for each class by training. In order to further reduce the complexity, split-VQ has been employed. The classification is achieved through vector quantization with a mean squared distortion measure in the LSP domain.

An interactive speech coding tool using LabVIEW

Abstract: Code Excited Linear Prediction (CELP) is a closed-loop analysis-by-synthesis speech coding algorithm that has been standardized in Federal Standard-1016. Variants of the CELP algorithm form the core of many speech coding standards that exist today. In this paper, we discuss the development of an interactive speech coding tool in National Instruments LabVIEW™ software for the Federal Standard-1016 CELP algorithm. A brief description of the speech coding algorithm and the features of the LabVIEW speech coding tool are presented. Illustrations demonstrating the use of the interactive software tool in analyzing the speech coding algorithm are provided. This tool can be used to teach the various modules of the CELP based speech coders to undergraduate and graduate students.

Towards optimal LPC spectral parameters estimation in standard CELP 4800 b/s speech coder

Abstract: The influence of nonuniform scalar quantization and the specific interpolation procedure of LSP parameters, as proposed in USA FED STD 1016 CELP 4800 b/s speech coder, to the LPC spectra of speech obtained by using four standard LPC methods: autocorrelation with Hamming window, covariance, modified covariance and lattice, is considered. Comparative experimental analysis is done referring to the results of three different spectral measures related to the RMS LOG spectral measure: likelihood ratio, cosh measure and cepstral distance. The experimental analysis shows that, in the sense of the global LPC spectral representation, the best results are obtained by using the modified covariance method. In this way, the orientation to the autocorrelation method with Hamming window, proposed in USA FED STD 1016 CELP coder, is not justified.