Learn More
This paper proposes a low complex forward adaptive loss compression algorithm that works on the frame by frame basis. Particularly, the algorithm we propose performs frame by frame analysis of the input speech signal, estimates and quantizes the gain within the frames in order to enable the quantization by the forward adaptive piecewise linear optimal(More)
In this paper, a simple and complete asympto-tical analysis is given for a mean square error (MSE) piecewise uniform polar quantizer (PUPQ). We show that PUPQ has the same performance as the asymptotic non-uniform polar quantizer (NPQ) and has implementation complexity between complexities of NPQ and uniform polar quantization. The goal of this paper is(More)
This paper addresses the problem of polar quantization optimization. Particularly, the aim of this investigation is to find the method for the optimal resolution-constrained polar quantizer design. The new iterative algorithm for determination of the optimal decision and representation magnitude levels and algorithm for optimization of number of phase cells(More)
This paper has two achievements. The first aim of this paper is optimization of the lossy compression coder realized as companding quantizer with optimal compression law. This optimization is achieved by optimizing maximal amplitude for that optimal companding quantizer for Laplacian source. Approximate expression in closed form for optimal maximal(More)
In this paper a detail analysis of speech coding algorithm based on forward adaptive technique is carried out. We consider an algorithm that works on frame-by-frame basis, where a frame consists of a certain number of speech samples. Buffering frame-by-frame an estimation of the gain defined as squared root of the frame variance is enabled. The information(More)
In this paper an exact and complete analysis of the Lloyd–Max's algorithm and its ini-tialization is carried out. An effective method for initialization of Lloyd–Max's algorithm of optimal scalar quantization for Laplacian source is proposed. The proposed method is very simple method of making an intelligent guess of the starting points for the iterative(More)
This paper proposes the novel model of scalar quantizer that combines two classical models, the model of scalar compandor and the model of Lloyd-Max's scalar quantizer. Particularly, the proposed model utilizes the advantages of the both models while tending to minimize their deficiencies. The performance analysis of the novel quantizer is carried out(More)
In this paper a new model for compression of Laplacian source is given. This model consists of hybrid quantizer whose output levels are coded with Golomb-Rice code. Hybrid quantizer is combination of uniform and nonuniform quantizer, and it can be considered as generalized quantizer, whose special cases are uniform and nonuniform quantizers. We propose new(More)
Previous studies of nonlinear prediction of speech have been mostly focused on short-term prediction. This paper presents long-term nonlinear prediction based on second-order Volterra filters. It will be shown that the presented predictor can outperform conventional linear prediction techniques in terms of prediction gain and “whiter”(More)