基于数据驱动的单通道语音增强方法研究.ppt

This table shows the result of the segmental SNR improvement under different input SNR conditions in various noise. denotes the MMSE spectral amplitude estimate method And Ref. B indicates the codebook-based MMSE methodFrom this table, we can see that the proposed method could get a better performance than the other two references in most cases. * Figure 2 describes the proposed method. We can see that The proposed method have two parts. One is offline training stage and the other is online enhancing stage. At training stage, the pre-enhanced speech is obtained through pre-processing. Then we can get the pre-enhanced cue. The clean cue is extracted from clean speech. And the noisy speech and the clean speech is one-to-one corresponding. At last, the pre-enhanced cue and the clean cue is used to train the codebook. At enhancing stage, we obtain the pre-enhanced speech first, then the online clean cue is estimated by weight codebook mapping with the trained codebook and online pre-enhanced cue.  * Figure 3 shows the scheme of weighted codebook mapping (WCBM) algorithm. * The way to …by wcm algorithm is introduced here. * after we get the online clean cue, which contain the speech and noise level deffirents and speech and noise correlation. We can enhance the noisy speech. * This table shows the result of the segmental SNR improvement under different input SNR conditions in various noise. denotes the MMSE spectral amplitude estimate method And Ref. B indicates the codebook-based MMSE methodFrom this table, we can see that the proposed method could get a better performance than the other two references in most cases. * This table gives the test results of PESQ But we can find that the proposed method performs better than the other two references, especially under the noisy condition with high inut SNR. * In table 3, we show the test results of log spectrum distance. According to the results in table 3, the proposed method performs better than Ref. A. However