低延迟码激励语音编码算法的最佳增益滤波器
The Optimal Gain Filter of LD-CELP
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摘要: 该文采用加权L-S算法、有限记忆算法以及BP神经网络算法分别与G.728标准使用的Levinson_Durbin(L-D)方法进行5样点激励增益滤波方案比较测试，发现编码效果均好于G.728。其中加权L-S方法语音编码效果最好,其平均分段SNR高出G.728算法0.76dB。用该方法评价了16样点激励矢量增益滤波器和20样点激励矢量增益滤波器，加权L-S方法同样效果最佳。Abstract: The recommendation G.728 depends on the Levinson-Durbin algorithm to update gain filter coefficients. In this topic, it is replaced by three different methods which are the weighted L-S recursive filter, the finite memory recursive filter and the BP neural network, respectively. Using these three gain filter the speech coding effect is all better than the G.728. The weighted L-S algorithm has the best result. Its average segment SNR is higher than the G.728 about 0.76dB. It is also used to evaluate the case that excitation vector is 16 and 20 samples respectively; the weighted L-S algorithm has similarly the best result.
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