# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 4 Performance Evaluation
### Objective Evaluation  
There are five objective evaluation tests for analyzing the performance of the proposed separation method. The first test was performed in an ideal acoustic room, and the dereverberation was not conducted. First, we only used the DOA estimates of sound sources to obtain the separated signal from the sparse component points, ignoring the non-sparse component points. The DNN was used to obtain the separated signal from the non-sparse component points. The multiple-sound-source separation method combing sparse and non-sparse component point recovery was utilized to obtain the separated signal from the whole TF points. Then, we compared the average PESQ of these separated signals. Average PESQ scores are presented with 95% confidence intervals. Conditions \"Sparse\" and \"Non-sparse\" represent the separated signals from only the sparse and the non-sparse component points, respectively; condition \"Joint-proposed\" is the signals separated from the whole TF points (sparse and non-sparse component points). When the source number is three and four, the source separation is 30\({}^{\circ}\) and 50\({}^{\circ}\), respectively. Moreover, the original sound source signal serves as the reference signal. The average PESQ results in Figure 8 show that the quality of the signal separated by the proposed method combining sparse and non-sparse component point recovery is higher than that of the separated signal from only sparse or non-sparse component points, which confirms the performance advantage of our proposed method. The second test was conducted in a reverberant room with \(T_{60}=300\) ms. The aim of this test was to evaluate the performance of the proposed method with or without dereverberation. The average PESQ scores of the signals separated by the above two methods are shown in Figure 9 with 95% confidence intervals. When the source number is two and three, source separation is 50\({}^{\circ}\) and 70\({}^{\circ}\), respectively. Condition \"With Dereverberation\" is the proposed method. Condition \"Without Dereverberation\" means that we directly separated the reverberant mixed signal without dereverberation. In theory, the dereverberation algorithm can maintain the components which benefit source separation (i.e., the direct components of the sound sources) and reduce the damage of reverberation to separation performance. As illustrated in Figure 9, the proposed method with dereverberation can achieve better performance than the proposed method without dereverberation. In the case of N = 3, the improvement of average PESQ is more significant. Specifically, the improvement of average PESQ can reach 0.14. Therefore, it proves that the NMF-based dereverberation method can help improve the perceived quality of separated signals. In general, the above tests verify the validity and rationality of the proposed separation method. Next, we present a series of experiments to compare the performance of our proposed method with the reference methods. The third test was performed in two reverberant rooms (i.e., Room1 with \(T_{60}=300\) ms and Room2 with \(T_{60}=450\) ms) to analyze the intelligibility of the separated signals from different separation methods. The test results are shown in Figure 10. Condition \"Proposed\" is the proposed separation method; condition \"JSNCS\" is the reference method proposed in [14]. Condition \"LCMV\" represents the separation method proposed in [9]. Figure 8: Average PESQ of the separated signals from sparse component points, non-sparse component points and the whole TF points. Figure 9: Average PESQ of signals separated by the proposed method with or without dereverberation. It can be found that the intelligibility of the separated signal obtained by the proposed method is higher than that of the reference method, especially in a high reverberant environment. It proves that our proposed method can achieve a satisfying quality of the separated signal.