Topic
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.
Papers published on a yearly basis
Papers
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01 Sep 2015TL;DR: The serial problem in atermarking and then encoding with the CELP codec was thereby reduced by using the proposed method which also ncreased the bit detection rate.
Abstract: This paper proposes the unification of the codeexcited linear prediction (CELP) codec process with watermarking based on formant tuning. The serial problem in atermarking and then encoding with the CELP codec was thereby reduced by using the proposed method which also ncreased the bit detection rate. We took advantage of two key properties: I) humans do not perceive alterations applied to formants and II) CELP and watermarking based on formant tuning methods utilize lineal prediction coefficients. We investigated the inaudibility and robustness of the proposed method by carrying out three different experiments using log-spectrum distance (LSD), the perceptual evaluation of speech quality (PESQ) and the bit detection rate (BDR). The results indicated that the proposed method satisfied the inaudibility requirement when watermarking was applied to the CELP codec, which increased the watermarking detection rate.
1 citations
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01 Jan 2003
TL;DR: An efficient rate selection algorithm that can be used to transcode speech encoded by any code excited linear prediction (CELP)-type codec into a format compatible with selectable mode vocoder via direct parameter transformation is proposed.
Abstract: In this paper, we propose an efficient rate selection algorithm that can be used to transcode speech encoded by any code excited linear prediction (CELP)-type codec into a format compatible with selectable mode vocoder (SMV) via direct parameter transformation. The proposed algorithm performs rate selection using the CELP parameters. Simulation results show that while maintaining similar overall bit-rate compared to the rate selection algorithm of SMV, the proposed algorithm requires less computational load than that of SMV and does not degrade the quality of the transcoded speech.
1 citations
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01 Jun 1990TL;DR: In designing the codebook, while the LBG method of clustering failed to converge, this paper has succeeded in finding a deterministic codebook based on a training set using the method of successive clustering.
Abstract: This paper presents a two-dimensional code excited linear prediction (CELP) method for image coding.
This method is a two-dimensional extension of the CELP systems commonly used for speech coding. The
decoder is identical to a conventional DPCM decoder. However, at the encoder, the input images are first
decomposed into disjoint blocks. A single codeword from a table of N codewords is used to represent the
vector of quantized residuals for each block. The encoder selects the appropriate codeword by reconstructing N
versions of the current block, using each of the N vectors of the codebook. The index of the codeword giving
the least distortion is then transmitted. In designing the codebook, while the LBG method of clustering failed
to converge, we have succeeded in finding a deterministic codebook based on a training set using the method
of successive clustering. The system has been extended by using adaptive prediction, where one of K possible
prediction filters is used for each block; the encoder chooses the prediction filter that results in the least mean
squared prediction error. An index is transmitted to the decoder indicating which prediction filter has been
used. With no additional overhead, K different codebooks can be used, corresponding to each of the prediction
filters. We have tested this system using five predictors. The five predictors were initially selected to give
good performance on different types of image material, e.g. edges of different orientation, and then refined by
minimizing the mean square prediction error on those pixels for which the initial predictor gave the lowest mean
square error.
1 citations
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TL;DR: A new adaptive source is proposed in which samples of the source have different gains according to their amplitudes by a two-tap pitch predictor and results show that peaky pulses at voiced onset and a burst of plosive sound are clearly reconstructed and that in voiced sound the excitation has the desirable peaky pulse characteristic and the pitch periodicity is well reproduced.
1 citations
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16 Oct 2006TL;DR: SNR is introduced by three different methods which are the weighted L-S recursive filter, the finite memory recursive filter and the BP neural network, respectively to estimate SNR so that the gain predictor can be separately optimized with the quantizer.
Abstract: The recommendation G.728 depends on the Levinson-Durbin algorithm to update gain filter coefficients. In this paper, it is introduced by three diferent methods which are the weighted L-S recursive filter, the finite memory recursive filter and the BP neural network, respectively. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. This paper proposes a scheme to estimate SNR so that the gain predictor can be separately optimized with the quantizer. Using these three gain filter the speech coding results are all better than the G.728. The weighted L-S algorithm has the best effect. Its average segment SNR is higher than the G.728 about 0.76dB. It is also used to evaluate the case that excitation vector is 16 and 20 samples respectively; the weighted L-S algorithm has similarly the best result.
1 citations