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.

Fine granularity scalability for MP-CELP based speech coding with HPDR technique

Abstract: In this work, based on the MP-CELP speech coding with HPDR technique, fine granularity scalability (FGS) is introduced by adjusting the amount of transmitted fixed excitation information. The FGS feature aim at changing the bit rate of the conventional coding more finely and more smoothly. Through performance analysis and computer simulation, the quality of scalability of the MP-CELP coding is presented with an improvement from conventional scalable MP-CELP. The HPDR technique is also applied to the MP-CELP to use for tonal language, meanwhile it can support the core coding rate of 4.2, 5.5, 7.5 kbps and additional scaled bit rates.

An 8-kb/s conjugate-structure algebraic CELP (CS-ACELP) speech coding

Abstract: The ITU-T issued the new recommendation G.729 in 1996, to realize a high-quality and low-delay speech coder at 8-kb/s. In this paper, the algorithm for conjugate-structure algebraic code-excited linear prediction (CS-ACELP) is discussed, and its central aspects are analyzed in detail. Topics covered include the special codebook structure, efficient codebook search strategies and speech improvement approaches.

Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations

Abstract: Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of samples. Ultimately, each prediction mode can be regarded as a set of very simple linear predictors, a different one for each pixel of a block. Therefore, a natural question that arises is whether one could use the theory of linear prediction in order to generate intra prediction modes that provide increased coding efficiency. However, such an interpretation of each directional mode as a set of linear predictors is too poor to provide useful insights for their design. In this paper, we introduce an interpretation of directional prediction as a particular case of linear prediction, which uses the first-order linear filters and a set of geometric transformations. This interpretation motivated the proposal of a generalized intra prediction framework, whereby the first-order linear filters are replaced by adaptive linear filters with sparsity constraints. In this context, we investigate the use of efficient sparse linear models, adaptively estimated for each block through the use of different algorithms, such as matching pursuit, least angle regression, least absolute shrinkage and selection operator, or elastic net. The proposed intra prediction framework was implemented and evaluated within the state-of-the-art high efficiency video coding standard. Experiments demonstrated the advantage of this predictive solution, mainly in the presence of images with complex features and textured areas, achieving higher average bitrate savings than other related sparse representation methods proposed in the literature.

Novel Pitch Detection Algorithm With Application to Speech Coding

Abstract: This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions.

Adaptive sound source vector quantization device, adaptive sound source vector inverse quantization device, and method thereof

Abstract: Disclosed is an adaptive sound source vector quantization device capable of improving quantization accuracy of adaptive sound source vector quantization while suppressing increase of the calculation amount in CELP sound encoding which performs encoding in sub-frame unit. In the device, a search adaptive sound source vector generation unit (103) cuts out an adaptive sound source vector of a frame length (n) from an adaptive sound source codebook (102), a search impulse response matrix generation unit (105) generates a search impulse response matrix of n n by using an impulse response matrix for each of sub-frames inputted from a synthesis filter (104), a search target vector generation unit (106) adds the target vector of each sub-frame so as to generate a search target vector of frame length (n), an evaluation scale calculation unit (107); calculates the evaluation scale of the adaptive sound source vector quantization by using the search adaptive sound source vector, the search impulse response matrix, and the search target vector.