We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the veri cation phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method...

In this paper, a new technique for lossy compression of halftone images is proposed based on the vector quantization technique. A conventional vector quantization encoder is modified such that it embeds a block-based error diffusion process and takes a HVS model into account during the compression. This modification significantly improves the visual performance of encoded images while the compr...

This paper studies tracking performance limitations for a networked feedback control system. In the system, the plant is a linear time invariant (LTI) SISO system and the measurement signal is received from a remote site through a network. The reference signal in the tracking problem is a step signal. The tracking performance is measured by an integral square error between the output of the pla...

In order to automatically obtain hierarchical knowledge representation from a certain data, an unsupervised learning method has been developed that overcomes two problems of the growing hierarchical self-organizing map (GHSOM) method, which uses the quantization error, the deviation of the input data, as evaluation measure of the growing maps: proper control of the growth process of each map is...

Visual information can help listeners to better understand what is said. In the speech coding domain, it will be shown that it allows to reduce the transmission rate of a classic vocoder (1,9 kbit/s instead of 2,4 kbit/s) by estimating audio parameters from video ones. In addition, vector quantization seems to be a good method to reduce the redundancy between some audio and visual coefficients....

The central idea of this paper is to propose a robust audio watermarking algorithm using statistical parameters and energy of the signal in the discrete wavelet domain and also using discrete cosine transform. The performance of the algorithm is provided by evaluating the performance parameters such as signal to noise ratio, normalized correlation, and bit error rate. In addition, the effects o...

In this paper, we introduce a soft vector quantization scheme with inverse power-function distribution, and analytically derive an upper bound of the resulting quantization noise energy in comparison to that of typical (hard-deciding) vector quantization. We also discuss the positive impact of this kind of soft vector quantization on the performance of machine-learning systems that include one ...

This paper provides a precise analytical study of the selection and modulus quantization of matching pursuit (MP) coefficients. We demonstrate that an optimal rate-distortion trade-off is achieved by selecting the atoms up to a quality-dependent threshold, and by defining the modulus quantizer in terms of that threshold. In doing so, we take into account quantization error re-injection resultin...

In this contribution a modified scheme for linear prediction analysis is presented which controls the degree of decorrelation by a parameter α. In consideration of this parameter the signal to noise ratio of a linear predictive coding scheme is investigated and finally maximized for open loop quantization. Also, the new parameter can be used to control the spectral shape of the quantization err...

Quantzzat~on can effectively reduce the huge amount of data with possibly small error (called quantzzatton error). In designing a quantizer using a portion of the data as a training data , the training algorithm tries to find a codebook that minimizes the quantization error measured in the training data. I t is known that , under several conditions, the minimized quantizat.ion error approaches ...