## 4 Results and Discussion
Figure 12 shows the SegSNR results in SNRs [-10, -5, 0, 5, 10, and 15] dB for the proposed CNMA-SBAPA in comparison with the LMS, traditional APA, RLS, DB-MWF, and MNMF-MVDR for real and simulated data in the presence of white noise. As seen, the proposed method had a superior performance in different ranges of SNRs in comparison with the rest of the works, namely a better noise elimination was reached via the proposed algorithm. For example, the proposed method enhanced the noisy speech signal with SNR = \(-\)10 dB to SegSNR = 1.35 dB in comparison with SegSNR = \(-\)4.58 dB in LMS, SegSNR = \(-\)3.21 dB in APA, SegSNR = \(-\)1.57 dB in RLS, SegSNR = \(-\)1.68 dB in DB-MWF, and SegSNR = \(-\)0.94 dB in MNMF-MVDR. Nevertheless, the quantitative criteria are not enough to properly evaluate a method, and both quantitative and qualitative criteria should be considered in the evaluations. Figure 11: The time-domain and spectrum representation for (**a**) white noisy signal and enhanced signal by the (**b**) least mean square (LMS), (**c**) APA, (**d**) recursive least square (RLS), (**e**)distributed multichannel Wiener filter (DB-MWF), (**f**) multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR), and (**g**) proposed CNMA-SBAPA for SNR = 0 dB. In addition, the proposed method was compared with previous works by qualitative criteria such as the PESQ, MOS, and STOI. We used 20 volunteers, where they listened first to the clean signal by the headset to have an idea of an excellent signal with a rating of 5 in the MOS scale and a noisy signal (before enhancement), which is the worst option in the MOS scale with a rating of 1. Then, the enhanced signal in a different range of SNRs were played for them, and they were asked to select a rate between 1 and 5 based on the Table 3. Figure 13 shows the PESQ, STOI, and averaged MOS criteria for the enhanced signal by the proposed method in comparison with previous works on real and simulated data for different ranges of SNRs in the presence of white noise. As seen, the proposed method had the best performance in comparison with previous works. For example, the PESQ score was 3.41 in the proposed method in comparison to 1.82 in LMS, 2.51 in APA, 2.73 in RLS, 2.93 in DB-MWF, and 3.1 in MNMF-MVDR, for SNR = 15 dB for simulated data. In addition, the STOI criteria was 0.89 in the proposed method in comparison to 0.73 in LMS, 0.77 in APA, 0.81 in RLS, 0.83 in DB-MWF, and 0.85 in MNMF-MVDR, for SNR = 15 dB. The other criteria for comparison was the average MOS rate, which was 3.5 in the proposed method in comparison to 2.5 in LMS, 2.7 in APA, 2.9 in RLS, 3.0 in DB-MWF, and 3.0 in MNMF-MVDR, for SNR = 15 dB. Therefore, the proposed method was superior for enhancing  
conditions was higher than the colored noisy scenarios. Therefore, the proposed CNMA-SBAPA method had superiority for the speech enhancement in comparison with LMS, traditional APA, RLS, DB-MWF, and MNMF-MVDR algorithms based on the quantitative SegSNR and qualitative PESQ, MOS, and STOI criteria, as well as the speed of convergence. Figure 13: The perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and averaged mean opinion score (MOS) comparison between the proposed CNMA-SBAPA and the LMS, traditional APA, RLS, DB-MWF, and MNMF-MVDR methods for (**a**) simulated and (**b**) real data by considering the white noise.