Optimal Multistage Vector Quantization ofLPC Parameters Over Noisy Channels (original) (raw)

Speech coding based upon vector quantization

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1980

With rare exception, all presently available narrow-band speech coding systems implement scalar quantization (independent quantization) of the transmission parameters (such as reflection coefficients or transformed reflection coefficients in LPC systems). This paper presents a new approach cdled vector quantization.

Speech spectral quantizers for wideband speech coding

European Transactions on Telecommunications, 2001

In this treatise a range of Line Spectrum Frequency (LSF) Vector Quantization (VQ) schemes were studied comparatively, which were designed for wideband speech codecs. Both predictive arrangements and memoryless schemes were investigated. Specifically, both memoryless Split Vector Quantization (SVQ) and Classified Vector Quantization (CVQ) were studied. These techniques exhibit a low complexity and high channel error resilience, but require high bit rates for maintaining high speech quality. By contrast, Predictive Vector Quantizers (PVQ) offer an enhanced Spectral Distortion (SD) performance, although they are sensitive to channel error propagation. It is shown that the family of so-called Safety-Net Vector Quantization (SNVQ) schemes offers a good design compromise, providing an extension to memory-based PVQ, and thereby improving the performances both over noisy and noiseless channels. Submission 1

An improved vector quantisation algorithm for speech transmission over noisy channels

1996

Abstract Vector quantisation (VQ) is a method widely used in low bit-rate coding and transmission of speech signals. Unfortunately, a single bit error in the transmitted index, due to noise in the transmission channel, could degrade perceived speech quality at the receiver quite dramatically as the reference vector retrieved by the corrupted index may differ greatly from the vector corresponding to the intended index.

Comparative study on vector quantization codebook generation algorithms for wideband speech coding

2012 International Conference on Green Technologies (ICGT), 2012

This paper presents a review and performance comparison on several vector quantization (VQ) codebook generation algorithms. The codebook generation algorithms discussed include K-means algorithm, Multi stage vector quantization (MSVQ) algorithm and Predictive split vector quantization (PSVQ)algorithm. The vectors under consideration are the Line Spectral Frequency (LSF) vectors obtained by transforming Linear Prediction Coefficients (LPCs) extracted during Code Excited Linear Prediction(CELP) coding of wideband speech. The study compares the algorithms based on their computational cost and the perceptual quality of resultant speech.

Vector quantization of LPC parameters in the presence of channel errors

1993

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the spectral envelope information of speech. In our earlier paper [l], we have reported on a vector quantizer using line spectral frequencies and shown that it can quantize LPC parameters in 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortion in the range 2-4 dB and no frame having specVal distortion greater than 4 dB. In this paper, we study the performance of this quantizer in the presence of channel errors and compare it with that of scalar quantizer. We also investigate the use of error correcting codes for improving the performance of the vector quantizer in the presence of channel errors.

Efficient Two-step Spectrum Quantization Methods for Low Rate Speech Coding

ISITA'94: International …, 1994

Line Spectral Frequencies (LSFs) are often used as parameters to represent the vocal tract filter in speech coders using linear prediction. We propose two new methods for the quantization of the LSPs, namely Combined Scalar-Vector Quantization (CSVQ) and Fine-Coarse Split Vector quantization (FCSVQ). Both of these methods are based on a two-step vector quantization scheme. The paper explains the principles of these methods, including training of the associated codebooks. It is shown that they can be implemented efficiently with negligible computational overhead compared to simple scalar quantization. Satisfactory performance of the new methods is verified through experimental tests using computer simulation.

Multi Switched Split Vector Quantization Of Narrowband Speech Signals

2008

Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to split vector quantization (SVQ), multi stage vector quantization(MSVQ) and switched split vector quantization (SSVQ) techniques. It has been proved from results that MSSVQ has better spectral distortion performance, lower computational complexity and lower memory requirements when compared to all the above mentioned product code vector quantization techniques. Computational complexity is measured in floating point operations (flops), and memory requirements is measured in (floats).

Vector quantization: A pattern-matching technique for speech coding

IEEE Communications Magazine, 1983

ECTOR QUANTIZATION (VQ), a new direction in source coding, has recently emerged as a powerful and widely applicable coding technique. It was first applied to analysis/synthesis of speech, and has allowed Linear Predictive Coding (LPC) rates to be dramatically reduced to 800 b/s with very slight reduction in quality, and further compressed to rates as low as 150 b/s while retaining intelligibility [ 1,2]. More recently, the technique has found its way to waveform coding [3-51, where its applicability and effectiveness is less obvious and not widely known. There is currently a great need for a low-complexity speech coder at the rate of 16 kb/s which attains essentially "toll" quality, roughly equivalent to that of standard 64-kb/s log PCM codecs. Adaptive DPCM schemes can attain this quality with low complexity for the proposed 3 2 kb/s CCITT standard, but at 16 kb/s the quality of ADPCM or adaptive delta modulation schemes is inadequate. More powerful methods, such as subband coding or transform coding, are capable of producing acceptable speech quality at 16kb/s but have a much higher implementation complexity.