In this paper, single channel speech separation (SCSS) techniques based on hidden Markov models (HMM) and vector quantization (VQ) are described and compared in terms of (a) signal-to-noise ratio (SNR) between separated and original speech signals, (b) preference of listeners, and (c) computational complexity. The SNR results show that the HMMbased technique marginally outperforms the VQ-based ...

where CF and VF are the filter capacitance and voltage amplitude. See Fig. 1(a). To compute the ratio Vq/VF we use voltage division. We make the crucial observation that to calculate the qubit damping we must analyze the circuit impedances at the qubit frequency. Because the qubit is off resonance from the measurement resonator, the measurement resonator’s impedance Zr is lower than the impedan...

Data hiding is a booming technique aiming to embed secret data into digital media for content authentication, annotation or copyright protection. Reversible data hiding is a special type of data hiding technique that guarantees the host digital media can be losslessly recovered after the secret data are correctly extracted. In the past several years, some reversible data hiding schemes for Vect...

Vector Quantization (VQ) and Subband Coding (SBC) are promising candidates for future generations of image coders : VQ is known to outperform scalar quantization (SQ) techniques, SBC overcomes the block structure problem of DCT transform coders and has better energy compaction properties. The paper compares the performances of two SBC-VQ schemes (a QMF-based SBC with trained-codebook VQ and a p...

Directly embedding the secret data into the VQ-compressed domain is practical for reducing storage and transmittal bandwidth. Many steganography techniques are currently exploited for the VQ index table. However, embedding strategies extend the amount of compression needed for embedding secret data and distort the original quality of the VQ decompressed image. To satisfy the essentials of incre...

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural infor...

Two methods to overcome the problems with large vector quantization (VQ) codebooks are lattice VQ (LVQ) and product codes. The approach described in this paper takes advantage of both methods by applying residual VQ with LVQ at all stages. Using LVQ in conjunction with entropy coding is strongly motivated by the fact that entropy constrained but structurally unconstrained VQ design leads to mor...

In the present paper we study the use of vector quantization in the BTC-VQ image compression system. We propose an inverted order of proceeding in the BTC-VQ algorithm, so that the interaction of coding the bit plane and the quantization data will be taken into consideration. The quality of the image depends radically on the codebook used in VQ. The use of frequencies in the selection of the in...

Image retrieval and image compression are each areas that have received considerable attention in the past. In this work, we present an approach for content-based image retrieval (CBIR) using vector quantization (VQ). Using VQ allows us to retain the image database in compressed form without any need to store additional features for image retrieval. The hope is that encoding an image with a cod...

The subject of this paper is the integration of the traditional vector quantizer (VQ) and discrete hidden Markov models (HMM) combination in the mixture emission density framework commonly used in automatic speech recognition (ASR). It is shown that the probability density of a system that consists of a VQ and a discrete classifier can be interpreted as a special case of a semicontinuous mixtur...