Forward Adaptation of Novel Semilogarithmic Quantizer and Lossless Coder for Speech Signals Compression
In this paper new semilogarithmic quantizer for Laplacian distribution is presented. It is simpler than classic A-law semilogarithmic quantizer since it has unit gain around zero. Also, it gives for 2 97 dB higher signal-to-quantization noise-ratio (SQNR) for referent variance in relation to A-law, and therefore it is more suitable for adaptation Forward adaptation of this quantizer is done on frame-by-frame basis. In this way G 712 standard is satisfied with 7 bits/sample. which is not possible with classic A-law. Inside each frame subframes are formed and lossless encoder is applied on subframes. In that way, double adaptation is done. adaptation on variance within frames and adaptation on amplitude within subframes. Joined design of quantizer and lossless encoder is done, which gives better performances. As a result, standard G 712 is satisfied with only 6.43 bits/sample. Experimental results, obtained by applying this model on speech signal, are presented. It is shown that experimental and theoretical results are matched very well (difference is less than 1 5%) Models presented in this paper can be applied for speech signal and any other signal with Laplacian distribution.
semilogarithmic quantizer lossless coder forward adaptive quantizer