Interframe LSF quantization for noisy channels

Eriksson, Thomas; Linden, J.; Skoglund, Jan

doi:10.1109/89.784102

Cited by 51 publications

(27 citation statements)

References 52 publications

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“…For both quan- tizers SVQ and TCVQ, the 16-th dimensional LSF vector is split into five parts of (3,3,3,3,4) dimensional subvectors. In order to make a fair comparison of the three quantizers, they should be compared with the same speech database for the reason that different databases can lead to radically different objective performance for the same quantization scheme [30]. The performance of SVQ and its ROM requirement are summarized in Table 8.…”

Section: Simulation Resultsmentioning

confidence: 99%

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

Kaddai

Halimi

2009

IEICE Trans. Inf. & Syst.

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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.

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Section: Simulation Resultsmentioning

confidence: 99%

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

Kaddai

Halimi

2009

IEICE Trans. Inf. & Syst.

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“…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.…”

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“…이 방법을 사용하면 LSF 데이터의 interframe 상관관계를 고려하기 때문에 SVQ보다 성능이 좋아지게 된다. 과거 frame과의 차이값을 양자화 할 때, 과거값에 대한 가중치는 autoregressive (AR) 계수 를 이용하는 것이 최적의 방법이라고 알려져 있다 [8] . 관측 가능한 과거 frame의 개수가 늘어날수록 현재 frame의 예측 성능이 점점 증가 하지만, 채널 에러에 더욱 민감할 뿐만 아니라 계산량 문제도 있다고 알 려져 있기 때문에 과거 하나의 frame 정보에 대한 상 관관계를 사용하는 것이 일반적이었다 [13] .…”

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

Jung¹,

Kim²

2012

The Journal of the Acoustical Society of Korea

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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.

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“…The source-channel optimized MSVQ interleaving scheme was tested as follows: A group of eight images comprised of Pepper, Zelda, Airplane, Boat, Bridge, Face, Man, and Speedboat were used for training a five-stage MSVQ according to (5) and (7). Blocks of size 8 8 are rearranged into vectors.…”

Section: B Image Transmissionmentioning

confidence: 99%

Multistage vector quantizer optimization for packet networks

Khalil

Rose

2003

IEEE Trans. Signal Process.

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Abstract-A multistage vector quantizer (MSVQ) based coding system is source-channel optimized for packet networks. Resilience to packet loss is enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless speech line spectral frequency (LSF) parameter quantization as well as block-based image compression. With LSF coding, a source-channel-optimized MSVQ is shown to yield gains of up to 2.0 dB in signal-to-ratio (SNR) over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Substantial gains were also obtained in the case of block-based image compression. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.Index Terms-Erasure channels, multistage vector quantizer, packet networks, source-channel coding.

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Interframe LSF quantization for noisy channels

Cited by 51 publications

References 52 publications

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

Low-Complexity Wideband LSF Quantization Using Algebraic Trellis VQ

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

Multistage vector quantizer optimization for packet networks

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