This paper investigates the use of a new hybrid vector quantizer called switched multistage vector quantization (SWMSVQ) technique using hard and soft decision schemes, for coding of narrow band speech signals. This technique is a hybrid of switch vector quantization technique and multistage vector quantization technique. SWMSVQ quantizes the linear predictive coefficients (LPC) in terms of the...

In this paper, we compared three kinds of similarity measures for DP-matching based query-by-humming music retrieval experiments. First, a DP matching-based algorithm is formulated using the similarity between a deltaPitch of an input humming and that of a song in the database. Then the three similarities are introduced: distance-based similarity, quantization-based similarity and fuzzy quantiz...

in this paper, we study the existence of extremal solutions forimpulsive delay fuzzy integrodifferential equations in$n$-dimensional fuzzy vector space, by using monotone method. weshow that obtained result is an extension of the result ofrodr'{i}guez-l'{o}pez cite{rod2} to impulsive delay fuzzyintegrodifferential equations in $n$-dimensional fuzzy vector space.

Storaging or transmission of speech signals at very low bit rate is a hot area in the field of speech processing. We used stochastic inter-frame interpolators and vector quantization (VQ) as a new method for developing a high quality 1200 BPS speech vocoder. The objective and subjecgtive test results show that performance of the new vocoder is compairable with 4800 BPS standard vocoders (as CELP).

Quantization is the process of representing a large set of input values with a much smaller set. In signal processing and image processing, Vector Quantization is a classical quantization which extends the scalar quantization to multi-dimensional space. It is widely used in many applications such as data compression, data correction, pattern recognition, and density estimation. This project pro...

An analog VLSI architecture for learning vector quantization (LVQ), with on-chip adaptation and dynamic storage of the analog templates, is presented. The architecture extends to Fuzzy ART and Kohonen self-organizing maps through digital programming. The analog memory and adaptive element of the LVQ cell comprise 6 MOS transistors and one capacitor, and provide for robust selfrefresh of the dyn...

A number of network features is used to describe normal and intrusive traffic patterns. However the choice of features is dependent on which pattern to be detected. In order to identify which network features are more important for a particular network pattern, we propose an automated feature weighting method based on a fuzzy subspace approach to vector quantization modeling that can assign a w...

In this paper, we present a novel hybrid keyword spotting system that combines supervised and semi-supervised competitive learning algorithms. The rst stage is a S-SOM (Semi-supervised SelfOrganizing Map) module which is speci cally designed for discrimination between keywords (KWs) and non-keywords (NKWs). The second stage is an FDVQ (Fuzzy Dynamic Vector Quantization) module which consists of...

This paper presents a review of vector quantization techniques used for encoding digital images. First the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis here is on the usefulness of the vector quantization when it is combined with conventi...