## 5 Conclusions  
Speech enhancement is an important application in the signal processing for smart meeting rooms. The aim of speech enhancement is denoising, dereverberation, or denoising-dereverberation at the same time. The speech enhancement is implemented as a pre-processing step to produce the proper signal in such an application as speaker localization, tracking, speech recognition, text-to-speech, estimation the number of speakers, etc. The speech enhancement algorithms are divided into the single and multi-channels methods. The single-channel algorithms are challenging in the speech enhancement processes because of the lack of suitable information in the denoising procedure. In contrast, the multi-channel algorithms increase the enhancement accuracy due to having more information but the computational complexity is increased. In this article, a multi-channel speech enhancement method was \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SNR(dB)**} & \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Speed of Convergence (Seconds)**} \\ \cline{3-8}  & & **White Noise** & **Babble Noise** & **Train Noise** & **Car Noise** & **Restaurant Noise** \\ \hline \multirow{4}{*}{**-10**} & **LMS** & 0.541 & 0.582 & 0.61 & 0.654 & 0.668 \\  & **APA** & 0.516 & 0.551 & 0.592 & 0.611 & 0.627 \\  & **RLS** & 0.422 & 0.468 & 0.496 & 0.539 & 0.546 \\  & **DB-MWF** & 0.586 & 0.612 & 0.652 & 0.673 & 0.695 \\  & **MNMF-MVDR** & 0.51 & 0.539 & 0.57 & 0.592 & 0.637 \\  & **CNMA-SBAPA** & **0.356** & **0.367** & **0.393** & **0.419** & **0.427** \\ \hline \multirow{4}{*}{**-5**} & **LMS** & 0.537 & 0.556 & 0.579 & 0.601 & 0.634 \\  & **APA** & 0.497 & 0.527 & 0.541 & 0.56 & 0.572 \\  & **RLS** & 0.403 & 0.429 & 0.447 & 0.482 & 0.506 \\  & **DB-MWF** & 0.545 & 0.593 & 0.615 & 0.636 & 0.658 \\  & **MNMF-MVDR** & 0.494 & 0.509 & 0.527 & 0.553 & 0.587 \\  & **CNMA-SBAPA** & **0.337** & **0.356** & **0.379** & **0.391** & **0.411** \\ \hline \multirow{4}{*}{**0**} & **LMS** & 0.516 & 0.538 & 0.562 & 0.568 & 0.595 \\  & **APA** & 0.473 & 0.482 & 0.502 & 0.536 & 0.539 \\  & **RLS** & 0.396 & 0.409 & 0.427 & 0.435 & 0.452 \\  & **DB-MWF** & 0.531 & 0.563 & 0.579 & 0.602 & 0.621 \\  & **MNMF-MVDR** & 0.485 & 0.498 & 0.516 & 0.531 & 0.546 \\  & **CNMA-SBAPA** & **0.318** & **0.329** & **0.35** & **0.358** & **0.362** \\ \hline \multirow{4}{*}{**5**} & **LMS** & 0.492 & 0.505 & 0.517 & 0.525 & 0.529 \\  & **APA** & 0.464 & 0.479 & 0.492 & 0.467 & 0.503 \\  & **RLS** & 0.388 & 0.395 & 0.401 & 0.412 & 0.418 \\  & **DB-MWF** & 0.507 & 0.512 & 0.544 & 0.565 & 0.586 \\  & **MNMF-MVDR** & 0.459 & 0.466 & 0.487 & 0.503 & 0.525 \\  & **CNMA-SBAPA** & **0.327** & **0.336** & **0.347** & **0.359** & **0.365** \\ \hline \multirow{4}{*}{**10**} & **LMS** & 0.488 & 0.498 & 0.509 & 0.521 & 0.538 \\  & **APA** & 0.451 & 0.467 & 0.483 & 0.499 & 0.507 \\  & **RLS** & 0.369 & 0.381 & 0.389 & 0.395 & 0.411 \\  & **DB-MWF** & 0.478 & 0.496 & 0.513 & 0.543 & 0.559 \\  & **MNMF-MVDR** & 0.437 & 0.458 & 0.479 & 0.491 & 0.516 \\  & **CNMA-SBAPA** & **0.305** & **0.328** & **0.339** & **0.352** & **0.368** \\ \hline \multirow{4}{*}{**15**} & **LMS** & 0.472 & 0.485 & 0.493 & 0.498 & 0.506 \\  & **APA** & 0.463 & 0.474 & 0.478 & 0.485 & 0.49 \\ \cline{1-1}  & **RLS** & 0.372 & 0.376 & 0.385 & 0.396 & 0.408 \\ \cline{1-1}  & **DB-MWF** & 0.443 & 0.455 & 0.462 & 0.481 & 0.494 \\ \cline{1-1}  & **MNMF-MVDR** & 0.41 & 0.427 & 0.448 & 0.463 & 0.48 \\ \cline{1-1}  & **CNMA-SBAPA** & **0.299** & **0.319** & **0.325** & **0.349** & **0.374** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The speed of convergence, in seconds, for the proposed CNMA-SBAPA in comparison with the LMS, traditional APA, RLS, DB-MWF, and MNMF-MVDR methods for white and colored noises on real data in different range of SNRs (the bold numbers are the best results). proposed based on the microphone array.