Efficient search and design procedures for robust multi-stage VQ of LPC parameters for 4 kb/s speech coding

LeBlanc, W.P.; Bhattacharya, Bhaskar; Mahmoud, S.A.; Cuperman, V.

doi:10.1109/89.242483

Cited by 152 publications

(60 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For transparent quantization performance, the traditional SVQ needs 24 bits/frame [5], the MSVQ needs 22 bits/frame [6], the MSMQ needs 18 bits/frame [9]. However, in CSVQ, it is only used 5 bits/frame, the drastic bits reduction is achieved compared to other methods.…”

Section: B Csvq Performance In the Klt Transform Domainmentioning

confidence: 99%

“…Assume that the CSVQ has m subvectors, each subvector with n measurements is quantized using j bits, thus the total number of codebook elements is equal to m×2 j ×n. The search complexity is defined as the number of arithmetic operations to obtain the quantized measurements [6], which is typically presented on the logarithmic scale.…”

Section: B Csvq Performance In the Klt Transform Domainmentioning

confidence: 99%

“…It's known that there is high correlation between two neighbouring LSP frames, and between adjacent LSP parameters within a frame, so the bit rates can be further reduced when both interframe and intraframe redundancy is removed. The exploitation of intraframe redundancy usually use the Split Vector Quantization (SVQ) [5] and Multi-Stage Vector Quantization (MSVQ) [6], which offer transparent quantization performance with realistic codebook storage and search characteristics at 22 to 24 bits/frame. Further compression can be obtained in principle by exploiting interframe correlation between sets of LSP parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters

Xiao¹,

Chen²,

Ya³

2011

IJITCS

View full text Add to dashboard Cite

Abstract-To achieve good reconstruction speech quality in a very low bit rate speech codecs, an efficient dimension reduction quantization scheme for the linear spectrum pair (LSP) parameters is proposed based on compressed sensing (CS). In the encoder, the LSP parameters extracted from consecutive speech frames are shaped into a high dimensional vector, and then the dimension of the vector is reduced by CS to produce a low dimensional measurement vector, the measurements are quantized using the split vector quantizer. In the decoder, according to the quantized measurements, the original LSP vector is reconstructed by the orthogonal matching pursuit method. Experimental results show that the scheme is more efficient than that of conventional matrix quantization scheme, the average spectral distortion reduction of up to 0.23dB is achieved in the DFT transform domain. Moreover, in the approximate KLT transform domain, this scheme can obtain transparent quality at 5 bits/frame with drastic bits reduction compared to other methods. Index Terms-

show abstract

Section: B Csvq Performance In the Klt Transform Domainmentioning

confidence: 99%

Section: B Csvq Performance In the Klt Transform Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters

Xiao¹,

Chen²,

Ya³

2011

IJITCS

View full text Add to dashboard Cite

show abstract

“…SVQ의 성능을 올리기 위한 방법 중에는 Switched SVQ (SSVQ) [5,6] , Multi-Stage VQ (MSVQ) [7] , Predictive SVQ (PSVQ) [8,9] 등이 있다. 그중 Differential Pulse Code Modulation (DPCM) 개념을 사용하는 PSVQ는 현재 frame과 이전 frame 간의 차이값을 양자화하는 방법이다.…”

unclassified

Quantization of LPC Coefficients Using a Multi-frame AR-model

Jung¹,

Kim²

2012

The Journal of the Acoustical Society of Korea

View full text Add to dashboard Cite

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using VectorQuantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

show abstract

“…Typically, an individual quantizer is designed and used for each sub-vector. The multistage vector quantization of the LSF parameters is suggested in [5]. Another efficient way with a small complexity is the algebraic vector quantization proposed by Xie and Adoul [6].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

Kaddai

Halimi

2009

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn this paper an algebraic trellis vector quantization (ATVQ) that introduces algebraic codebooks into trellis coded vector quantization (TCVQ) structure is presented. Low encoding complexity and minimum memory storage requirements are achieved using the proposed approach. It exploits advantages of both the TCVQ and the algebraic codebooks to know the delayed decision, the codebook widening, the low computational complexity and the no storage of codebook. This novel vector quantization scheme is used to encode the wideband speech line spectral frequencies (LSF) parameters. Experimental results on wideband speech have shown that ATVQ yields the same performance as the traditional split vector quantization (SVQ) and the TCVQ in terms of spectral distortion (SD). It can achieve a transparent quality at 47 bits/frame with a considerable reduction of memory storage and computation complexity when compared to SVQ and TCVQ.

show abstract

Efficient search and design procedures for robust multi-stage VQ of LPC parameters for 4 kb/s speech coding

Cited by 152 publications

References 15 publications

An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters

An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters

Quantization of LPC Coefficients Using a Multi-frame AR-model

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

Contact Info

Product

Resources

About