低延遲碼激勵(lì)語音編碼算法的最佳增益濾波器
The Optimal Gain Filter of LD-CELP
-
摘要: 該文采用加權(quán)L-S算法、有限記憶算法以及BP神經(jīng)網(wǎng)絡(luò)算法分別與G.728標(biāo)準(zhǔn)使用的Levinson_Durbin(L-D)方法進(jìn)行5樣點(diǎn)激勵(lì)增益濾波方案比較測(cè)試，發(fā)現(xiàn)編碼效果均好于G.728。其中加權(quán)L-S方法語音編碼效果最好,其平均分段SNR高出G.728算法0.76dB。用該方法評(píng)價(jià)了16樣點(diǎn)激勵(lì)矢量增益濾波器和20樣點(diǎn)激勵(lì)矢量增益濾波器，加權(quán)L-S方法同樣效果最佳。
-
關(guān)鍵詞:
- 增益濾波器;加權(quán)L-S;有限記憶;G.728;信噪比估計(jì)
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. -
計(jì)量
- 文章訪問數(shù): 2440
- HTML全文瀏覽量: 92
- PDF下載量: 608
- 被引次數(shù): 0
下載:
