Qiang Xiao; Liang Chen; Ya Wang

An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters

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Author(s)

Qiang Xiao ^1,* Liang Chen ¹ Ya Wang ¹

1. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2011.01.03

Received: 3 May 2010 / Revised: 24 Aug. 2010 / Accepted: 2 Dec. 2010 / Published: 8 Feb. 2011

Index Terms

Low bit rate speech coding, Line Spectrum Pair (LSP), Compressed Sensing (CS), Discrete Fourier Transform (DFT), Karhunen-Loeve Transform (KLT)

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.

Cite This Paper

Qiang Xiao, Liang Chen, Ya Wang, "An Efficient Dimension Reduction Quantization Scheme for Speech Vocal Parameters", International Journal of Information Technology and Computer Science(IJITCS), vol.3, vo.1, pp.18-25, 2011. DOI: 10.5815/ijitcs.2011.01.03

Reference

[1] G.Gwénaël, C.François, R.Bertrand, et al. New NATO STANAG narrow band voice coder at 600bits/s. IEEE ICASSP 2006, pp.689-692.

[2] I.A.Gerson, M.A.Jasiuk. Vector sum excitation linear prediction (VSELP) speech coding at 8 kbps. IEEE ICASSP 1990, pp.461-464.

[3] T.Moriya, M.Honda. Speech coder using phase equalization and vector quantization. IEEE ICASSP 1986, pp.1701-1704.

[4] Y.Shoham. Cascaded likelihood vector coding of the LPC information. IEEE ICASSP 1989, pp.160-163.

[5] K.K.Paliwal, B.S.Atal. Efficient vector quantization of LPC parameters at 24bits/frame. IEEE Trans. on speech and audio processing, Vol. 1, No. 1, pp.3-14, 1993.

[6] W.P.LeBlanc, B.Bhattacharya, S.A.Mahmoud, et al. Efficient search and design procedures for robust Multistage VQ of LPC parameters for 4 kb/s speech coding. IEEE Trans. on speech and audio processing, Vol. 1, No. 4, pp.373-385, 1993.

[7] Xia Zou, Xiongwei Zhang. Efficient coding of LSF parameters using multi-mode predictive multistage matrix quantization. IEEE ICSP 2008, pp.542-545.

[8] S.Subasingha, M.N.Murthi, S.V.Andersen. On GMM kalman predictive coding of LSFs for packet loss. IEEE ICASSP 2009, pp.4105-4108.

[9] S.özaydin, B.Baykal. Matrix quantization and mixed excitation based linear predictive speech coding at very low bit rates. Speech communication, Vol. 41, pp.381-392, 2003.

[10] Xia Zou, Xiongwei Zhang, Yafei Zhang. A 300bps speech coding algorithm based on multi-mode matrix quantization. IEEE WCSP 2009, pp.1-4.

[11] D.L.Donoho. Compressed sensing. IEEE Trans. on information theory, Vol. 52, No. 4, pp.1289-1306, 2006.

[12] T.V.Sreenivas, W.B.Kleijn. Compressed sensing for sparsely excited speech signals. IEEE ICASSP 2009, pp.4125-4128.

[13] R.G.Baraniuk. Compressive sensing. IEEE Signal Processing Magazine, Vol. 24, No. 4, pp.118-121, 2007.

[14] E.Candès, T.Tao. Decoding by linear programming. IEEE Trans. on information theory, Vol. 51, No. 12, pp.4203-4215, 2005.

[15] J.A.Tropp, A.C.Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. on information theory, Vol. 53, No. 12, pp.4655-4666, 2007.

[16] C.E.Davila. Blind adaptive estimation of KLT basis vectors. IEEE Trans. on signal processing, Vol. 49, No. 7, pp.1364-1369, 2001.

[17] F.Gianfelici, G.Biagetti, P.Crippa, et al. A novel KLT algorithm optimized for small signal sets. IEEE ICASSP 2005, pp.405-408.

[18] A.Zymnis, S.Boyd, E.Candès. Compressed sensing with quantized measurements. IEEE Signal Processing letters, Vol. 17, No. 2, pp.149-152, 2010.

International Journal of Information Technology and Computer Science (IJITCS)