# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 5 Experimental Results and Discussions
### Performance and Analysis
Here, two different \(\alpha\) settings, as illustrated in Figure 4, were used to represent the standard and nonstandard loudspeakers assignment situations and the two \(\alpha\) sets for loudspeakers \(p_{1}\)-\(p_{4}\) are \(\{190^{\circ},120^{\circ},60^{\circ},350^{\circ}\}\) and \(\{225^{\circ},135^{\circ},45^{\circ},315^{\circ}\}\), separately. As the reverberation of near-end speech was not taken into consideration, the near-end signal was directly mixed with the recorded echo signal at SER = 5 dB to generate the near-end microphone signal. The spectrogram of the real recordings with standard or nonstandard loudspeaker layouts processed by different algorithms are plotted in Figures 5 and 6. Figure 5 represents the standard loudspeakers assignment situation; the nonstandard scene is shown in Figure 6. The ERLE and PESQ scores of two models were also presented in the two figures. As shown in these two figures, both the D-format and B-format models performed well in the standard situation. Note that in the real experiment, the deviation of angle and distance between the loudspeakers and microphone inevitably existed. Although the decoding rules were the same as the training stage, the contribution of each loudspeaker signal for the echo signal was different from the standard condition due to the existence of the deviation. This can be the main reason for the performance degradation of the D-format model. Besides, the characteristics of each loudspeaker can also affect the produced echo. In the unknown decoding rule scene, the performance of the B-format model was much superior than the D-format model, indicating the greater robustness of the B-format model for practical applications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Algorithms** & \multicolumn{3}{c}{**ERLE (dB)**} & \multicolumn{3}{c}{**PESQ**} \\ \hline SER(dB) & **-** & **0** & **5** & **10** & **15** & **0** & **5** & **10** & **15** \\ \hline \multirow{4}{*}{RT\({}_{60}\) = 0.3 s} & Unprocessed & - & - & - & - & 1.85 & 2.15 & 2.46 & 2.71 \\  & PBFDLMS & 14.54 & 16.13 & 16.00 & 13.89 & 2.46 & 2.71 & 2.85 & 2.93 \\  & Singlech Model & 57.94 & 58.88 & 56.44 & 51.96 & 2.66 & 3.02 & 3.27 & 3.51 \\  & D-format Model & **62.30** & **60.72** & **58.40** & **53.60** & 2.81 & 3.15 & **3.37** & 3.63 \\  & B-format Model & 61.49 & 59.91 & 57.57 & 52.30 & **2.85** & **3.18** & 3.34 & 3.63 \\ \hline \multirow{4}{*}{RT\({}_{60}\) = 0.6 s} & Unprocessed & - & - & - & 1.79 & 2.18 & 2.39 & 2.71 \\  & PBFDLMS & 13.63 & 12.54 & 13.04 & 14.08 & 2.44 & 2.57 & 2.74 & 2.97 \\  & Singlechn Model & 56.32 & 57.66 & 56.25 & 52.36 & 2.66 & 3.02 & 3.27 & 3.44 \\  & D-format Model & **58.53** & **59.68** & **57.62** & **53.83** & 2.63 & 2.95 & 3.27 & 3.50 \\  & B-format Model & 57.34 & 58.56 & 56.34 & 52.44 & **2.67** & **3.02** & **3.31** & **3.53** \\ \hline \multirow{4}{*}{RT\({}_{60}\) = 0.9 s} & Unprocessed & - & - & - & 1.86 & 2.09 & 2.42 & 2.72 \\  & PBFDLMS & 11.31 & 13.02 & 12.75 & 13.55 & 2.23 & 2.44 & 2.76 & 2.99 \\ \cline{1-1}  & Singlechn Model & 57.26 & 56.18 & 56.53 & 52.14 & 2.46 & 2.82 & 3.18 & 3.46 \\ \cline{1-1}  & D-format Mode & **58.82** & **58.59** & **56.98** & **53.62** & 2.52 & 2.89 & 3.23 & 3.50 \\ \cline{1-1}  & B-format Model & 57.56 & 56.86 & 56.88 & 52.63 & **2.57** & **2.95** & **3.26** & **3.52** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons among different algorithms in different SERs and the standard-surround set-up conditions.