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	Quantization of LSF parameters based on the linear predictive adaptive Gaussian mixture model
	Zhao Yonggang ; Tang Kun ; Cui Huijuan
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical/ adaptive codes covariance matrices Gaussian distribution linear predictive coding speech codecs speech coding vector quantisation/ linear spectrum frequency LSF parameter quantization scheme Gaussian mixture model linear predictive adaptive GMM reconstruction speech coding speech codecs first-order interframe prediction diagonal covariance matrix random Gaussian distribution vector quantization/ B6130C Speech and audio coding B6220M Codecs, coders and decoders B0210 Algebra B0240Z Other topics in statistics
中文摘要	An efficient linear spectrum frequency (LSF) parameter quantization scheme was developed to achieve good reconstruction speech quality in low bit rate speech codecs. In the algorithm, the mean values of the LSF parameters are removed with a first-order inter-frame prediction then used to reduce the inter frame redundancy. The residual vectors after prediction are modeled using the Gaussian mixture model (GMM) with diagonal covariance matrices and then quantized using random Gaussian distribution vector quantization. The means and weights from the GMM are then used in a linear predictive adaptive LSF quantization algorithm. Tests show that the initial algorithm using 20 bit/frame outperforms the linear predictive split vector quantization using 22 bit/frame. The adoptive algorithm gives even better results than the initial algorithm.
语种	中文 ; 中文
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/11434]
专题	清华大学
推荐引用方式 GB/T 7714	Zhao Yonggang,Tang Kun,Cui Huijuan. Quantization of LSF parameters based on the linear predictive adaptive Gaussian mixture model[J],2010, 2010.
APA	Zhao Yonggang,Tang Kun,&Cui Huijuan.(2010).Quantization of LSF parameters based on the linear predictive adaptive Gaussian mixture model..
MLA	Zhao Yonggang,et al."Quantization of LSF parameters based on the linear predictive adaptive Gaussian mixture model".(2010).

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