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.

Coding image sequence intensities along motion trajectories using EC-CELP quantization

Abstract: The goal of this work is to investigate a high quality, low bit rate image sequence coding scheme which takes full advantage of the temporal redundancies. Such schemes will be important for high-compression video coding, such as the one studied in MPEG-4. In conventional image sequence coding, a motion-compensated DPCM configuration is used to remove the temporal redundancies. The performance of the DPCM for such highly correlated sources at rates below one bps degenerates substantially. We have recently shown that for a highly correlated Gauss-Markov source, practical near rate-distortion function performance is possible. We used the entropy-constrained code-excited linear predictive (EC-CELP) quantization scheme to obtain such performances. Hence for low rate image sequence coding, to improve the rate-distortion performance, an EC-CELP configuration can replace the DPCM configuration to quantize the image intensities along motion trajectories. To apply EC-CELP quantization, we first investigate the methods of motion trajectory estimation and propose a temporal block motion compensation configuration. Then, the performance advantage of EC-CELP configuration over the conventional DPCM structures is shown. >

On the behaviour of reduced complexity code-excited linear prediction (CELP)

Abstract: In this paper, we propose a simplification of the model introduced by B.S. Atal and M.R. Schroeder for a code-excited linear predictor. Our scheme is based on a two-step search of the optimum code-word. In the first step, a sub-set in the code-book is selected by using a simple distance measure and taking into account the selections already made for the previous frames of speech. In the second step, the original model of Atal and Schroeder, including perceptual weighting, is applied to the sub-set in order to make the final selection of the optimum code-word. The proposed scheme reduces the computational load involved in the original one, and our preliminar results indicate that the quality of the synthetic speech is fairly good.

One-Pulse FEC Coding for Robust CELP-Coded Speech Transmission Over Erasure Channels

Abstract: In this paper, we present an improved quantization scheme for the redundancy data of a forward error correction (FEC) technique proposed for the transmission of code-excited linear prediction (CELP)-coded speech over erasure channels. The use of a FEC-based error protection scheme is motivated by the well-known fact that, after a frame erasure, the previous excitation is not available and a desynchronization between the encoder and the decoder long-term prediction (LTP) filters appears, causing an additional distortion which is propagated to subsequent frames. LTP synchronization can be recovered by means of a single-pulse representation of the previous excitation. No additional delay is introduced by this technique which only requires a small transmission bandwidth increase. In this paper, we focus on the efficient encoding of this pulse. Thus, an optimization procedure, which takes into account the overall synthesis error, is proposed in order to provide better pulse-position and pulse-amplitude quantization codebooks. Moreover, by extending the previous procedure, an efficient joint position-amplitude quantization can be obtained. Objective quality tests applied to our proposal show that, by means of the proposed codebooks, the number of bits required to represent the resynchronization pulse is effectively reduced. In addition, a discontinuous transmission mechanism is derived from the cost functional used during joint position-amplitude quantization, further reducing the bit-rate.

Dereverberation of speech signals based on linear prediction.

Abstract: This paper proposes an algorithm for the blind dereverberation of speech signals based on a two-channel linear prediction. Traditional dereverberation methods usually achieve good performance when the input signal is white noise. However, when dealing with colored signals generated by an autoregressive (AR) process such as speech, the generating AR process is deconvolved causing excessive whitening of the signal. This paper proposes a blind dereverberation algorithm that recovers speech signals suffering from deterioration due to the reverberation in a room. We overcome the whitening problem faced by traditional methods by estimating the generating AR process and applying this estimated AR process to the whitened signal. Simulation results show the great potential of the proposed method.