## 4 Evaluation
### Objective Evaluation
#### 4.1.1 Simulated Environment  
First, by evaluating the same test data in s environment, the average PESQ scores of fixed threshold method and our proposed approach virus different source numbers and separations are shown in Figure 6. Condition SCS is the result extracted by the fixed threshold (i.e., \(\Delta\mu=\mu_{0}=8^{\circ}\)) sparse component separation method, while Pro-SCS is the proposed approach of sparse components with a dynamic threshold, and condition Pro-BSS is the proposed sparse and non-sparse component separation method. Figure 6a-d represents the result for separations \(\{30^{\circ},40^{\circ},50^{\circ},\,60^{\circ}\}\). It can be seen that the two proposed separation methods reach a higher score, especially for _source number_\(\geqslant 3\). In addition, Pro-BSS reaches the highest score, and the average scores are all above 2 for all source numbers, which indicates a better perceptual quality of the proposed method compared to the fixed threshold separation approach.  
Figure 6: Perceptual evaluation of speech quality (PESQ) results. Error bars represent 95% confidence intervals; (**a–d**) represent the results for separations \(\{30^{\circ},\,40^{\circ},\,50^{\circ},\,60^{\circ}\}\). SCS: sparse component separation; Pro-SCS: the proposed approach of sparse components with a dynamic threshold; Pro-BSS: the proposed sparse and non-sparse component separation method.  
The corresponding results are shown in Figures 7 and 8, respectively. Condition mixture (W) represents the B-format input signal \(S_{W}\). The SDR and SIR results follow a similar trend to the PESQ results. Overall, it can be concluded that the proposed method obtains a great improvement in extracted sources.  
Eventually, for the determined case (i.e., \(K=3\)), we conducted a comparison with four other existing approaches: (a) spatio-temporal ICA [32] applied using a single (recording from \(M_{i}\) using channel \(W_{i}\), \(X_{i}\), \(Y_{i}\)) B-format speech mixture (S-ICA); (b) spatio-temporal ICA applied using a dual (recording from \(M_{i}\) using channel \(W_{i}\), \(X_{i}\), \(Y_{i}\) and corresponding channels of \(M_{j}\)) B-format speech mixture (D-ICA); (c) source DOA-based BSS using single coincident microphone recording (S-BSS) [28]; and DOA-based collaborative BSS (CBSS) [20] using a pair of coincident spatial microphones. Note that the reference mixture (W) is an unprocessed speech mixture (W channel of the B-format recording) used for indicating the worst quality. It should be noted that there are still many good algorithms, like the independent vector analysis (IVA)-based method [33; 34], the independent low-rank matrix analysis (ILRMA)-based method, and so on [35]. Their methods focus on audio source separation, while we prefer the case with all speech sources, so only a few algorithms are chosen for comparison.  
Figure 7: Signal-to-distortion ratio (SDR) results. Error bars represent 95% confidence intervals; (**a–d**) represent the results for separations \(\{30^{\circ},40^{\circ},50^{\circ},60^{\circ}\}\).  
From Figure 9, the proposed BSS approach outperforms the other BSS techniques based on the PESQ measure. It should be noted that Figure 9a-c are calculated by different references in order to compare the separated speech using different methods under the same acoustic condition. In detail, for the reverberant conditions, the reference is selected as the clean speech with the same level of reverberation rather than anechoic clean speech. The major improvement (approximately 1 against the third best) is achieved by the proposed dynamic threshold-based sparse components and stability-based non-sparse components separation. Specifically, compared with C-BSS, we achieve a better perceptual quality by using only a B-format microphone, while C-BSS adopts a pair.