A variable bit rate multiband excited linear predictive speech coder is proposed in this paper. Speech signal is compressed in different bit rates ranging from 0.88 kbps to 2.6 kbps according to the mode of operation and the optimum V/UV transition frequency. An average bit rate of 1.24 kbps is achieved. The proposed speech coder improves the speech quality by splitting the non-stationary speec...

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. We consider here the usage of multiple description uniform sca...

The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, li...

It is important to produce automatic speech recognition (ASR) systems that use as few computational and memory resources as possible, especially in low-memory/low-power environments such as for personal digital assistants. One way to achieve this is through parameter quantization. In this work, we compare a variety of novel subvector clustering procedures for ASR system parameter quantization. ...

Free scalar field theory on a flat spacetime can be cast into a generally covariant form known as parametrised field theory in which the action is a functional of the scalar field as well as the embedding variables which describe arbitrary , in general curved, foliations of the flat spacetime. We construct the path integral quantization of parametrised field theory in order to analyse issues at...

Differential pulse-code modulation (DPCM) is recently coupled with uniform scalar quantization (SQ) to improve the rate-distortion (RD) performance for the block-based quantized compressive sensing (CS) of images. In this framework, for each block’s CS measurements, a prediction is generated based on the reconstructed CS measurements of the previous blocks and subtracted from measurements of th...

This paper presents a vector quantization system that limits the maximum distortion introduced to a pre-selected threshold value. This system uses a recently introduced variation of the L 1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected threshold. The vectors are rst coded using the new distortion measure. The quantization error vectors i...

In this work we study the topic of high-resolution adaptive sampling of a given deterministic signal and establish a clear connection to classic analyses of high-rate quantization. More specifically, we formulate solutions to the task of optimal high-resolution sampling of one-dimensional signals, which are shown as the counterparts of well-known results for high-rate scalar quantization. Our r...

It is important to produce automatic speech recognition (ASR) systems that use as few computational and memory resources as possible, especially in low-memory/low-power environments such as for personal digital assistants. One way to achieve this is through parameter quantization. In this work, we compare a variety of novel subvector clustering procedures for ASR system parameter quantization. ...

We consider the task of lossy compression of high-dimensional vectors through quantization. We propose the approach that learns quantization parameters by minimizing the distortion of scalar products and squared distances between pairs of points. This is in contrast to previous works that obtain these parameters through the minimization of the reconstruction error of individual points. The prop...