Split Vector Quantization Method Research Articles

Design and Comparison of Vector Quantization Codebooks for Narrowband Speech Coding

Vector quantization codebook algorithms are used for coding of narrow band speech signals. Multi-stage vector quantization and split vector quantization methods are two important techniques used for coding of narrowband speech signals and these methods are very popular due to the high bit rate minimization during coding of the signals. This paper presents performance measurements of multistage vector quantization and split vector quantization methods. We used line spectral frequencies for coding of the speech signals in codebook tables so as to ensure filter stability after quantization. The codebooks were generated by using the Linde-Buzo-Gray (LBG) algorithm. The tests were performed by selecting large amount of input data in training and test stages and to evaluate noise robustness of the methods, both noisy and clean speech signals were used. As a result, different codebooks were designed and tested in many stages and different bit rates to measure quantization performance. It is measured in terms of spectral distortion evaluation. We obtained the best result in 24bit multistage vector quantization codebook with a spectral distortion less than 1 dB for clean speech training data input. When we compared multistage and split vector quantization codebook spectral distortion results, multistage codebooks gave better performance in each option.

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate-distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.

Conditional PDF-Based Split Vector Quantization of Wideband LSF Parameters

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The commonly used split vector quantization (SVQ) method is inferior to unconstrained quantization due to independent coding of the split subvectors, resulting in a coding loss. In this paper, we propose a conditional pdf-based split vector quantization (CSVQ) method to recover the coding loss. The CSVQ method is developed assuming the line spectrum frequency source distribution as a multivariate Gaussian and the subvectors are quantized sequentially to exploit the correlation between the subvectors. The new CSVQ method is evaluated for wideband speech LSF quantization; CSVQ is shown to outperform traditional SVQ and provide comparable performance to the recently proposed switched split vector quantization (SSVQ) method. In addition, the transform domain SVQ method is also realized to show that its performance is limited by the distance measure used in the transform domain. </para>

Split vector quantization of LSF parameters with minimum of dLSF constraint

In speech coding, the spectral envelope of an analysis frame is often represented by line spectral frequencies (LSFs). LSFs are estimated from given linear predictive coefficients (LPCs) and can be transformed back to corresponding LPCs without loss of information. The authors present two improved split vector quantization (SVQ) methods for line spectral frequency (LSF) parameters. By using these methods jointly, the codewords and quantization results conserve a given minimum difference LSF (dLSF), although they are trained and quantized with a weighted distance measure. Experimental results show that the proposed methods are more effective than conventional SVQ methods, because the total training error and number of outliers due to quantization are all reduced.