线谱对矢量量化中的码本设计

研究ＧＬＡ算法和随机松驰算法设计的线谱对码本性能以及两种算法的特点，训练数据采用了云均值和一阶滑动平均模型预测的误差信号，随机松驰算法选用简化的解码器扰动算法，传统观点认为随机松驰算法比ＧＬＡ算法每适量索引少用１ｂｉｔ．实验结果表明，在小训练数据量时的确如此，但在大数据量时它们的性能相差不大。在线谱对的码本设计中，随机松驰算法设计的码本信噪比提高很小。

Codebook Design for VQ of LSP

The performance of codebooks designed for LSP with GLA and stochastic relaxation(SR) algorithm is investigated, and the features of two algorithms are discussed. The first order moving average prediction residual vectors of the mean removing LSP is used as the training sequence. The SR algorithm applied is a simplified SR algorithm for the decoder perturbation. The traditional conclusion is that SR saves GLA 1 bit per vector index. The experiment shows that the conclusion is right for a small amount of training sequence, but the difference in their performances is slight for a large amount of training sequence. The SNR improvement of codebook designed with SR algorithm is little when a LSP quantizer is designed.