# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 5 Experimental Results and Discussions
### Performance and Analysis  
The performance of the proposed GCRN-based AEC algorithm trained with B-format data (denoted as B-format Model) was firstly analyzed through comparison with the traditional PBFDLMS with the Wiener post-filtering method. Besides, we also trained a model using the D-format signals as the reference signal (denoted as D-format Model). For this model, the standard 5.1 surround sound set-up, as mentioned in Section 5.1, was used, and the decoding rules for this model were fixed. As for the B-format model, the decoding rules are in accordance with the loudspeaker layouts. Moreover, a model using only one  
Figure 4: Location of microphones and loudspeakers. channel B-format data and the microphone signal (denoted as Singlechn Model) as inputs was also performed to compare with the B-format model. The performance of the B-format and D-format model was firstly tested in standard 5.1 surround sound set-up condition without the center and the subwoofer channels. One test set generated with standard loudspeaker layouts was used, where the \(\alpha\) sets were the same as the ones used to train the D-format model. The PESQ and ERLE results in this situation are shown in Table 2. The RT\({}_{60}\) value was set to 0.5 s in the far-end room. From Table 2, one can see that in the standard loudspeaker assignment condition, the D-format model outperformed the B-format model in all conditions in terms of the ERLE criterion, because the tested surround sound set-up matched the training dataset of the D-format model completely. Both models had similar performances in terms of the PESQ criterion. To further evaluate the generalization capability of GCRN-based AEC algorithm to unseen RIRs and loudspeaker layouts, different \(\alpha\) settings were used to generate the RIRs in the near-end room. As shown in Figure 4, the \(\alpha\) angle for loudspeaker \(p_{1}\) was randomly sampled from \([186^{\circ},261^{\circ}]\) with \(15^{\circ}\) interval, and similarly, the \(\alpha\) ranges for \(p_{2},p_{3},p_{4}\) were set to be \([99^{\circ},174^{\circ}]\), \([9^{\circ},84^{\circ}]\) and \([276^{\circ},351^{\circ}]\) with \(15^{\circ}\) interval, respectively. The room dimensions were the same as the ones used in the training set. Note that the \(\alpha\) settings were different from that in the training sets, thus no RIRs were overlapped between the training and test sets. In this experiment, the RT\({}_{60}\) value was set to 0.5 s in the far-end room, and we tested the performance of each algorithm in different RT\({}_{60}\) values (0.3 s, 0.6 s, 0.9 s) of the near-end room. The PESQ and the ERLE results for different SER conditions are shown in Table 3. One can find that the GCRN-based surround AEC method performed much better than the other algorithms in every test condition. For the D-format model, the decoding rules mismatched with ones used in the training procedure, which is the main reason for its performance degradation. As for the one-channel model, it seemed that one-channel information was insufficient for the model to achieve the best performance. Besides, compared with Tables 2 and 3, one can find that the D-format model performed much worse in the nonstandard surround set-up, while the B-format model had similar performance in each test condition. To test the performance of the proposed method in the real acoustic environment, real-world experiments were conducted in one meeting room. The RT\({}_{60}\) values of the far-end room and near-end room were about 0.8 s and 0.25 s, respectively. The sizes of the two meeting rooms were about \(6.2\times 4.6\times 2.7\) m\({}^{3}\) and \(4.2\times 4.1\times 3.3\) m\({}^{3}\), separately. In the far-end room, the distance between the speaker and Ambisonics microphone was about 0.85 m. The recorded B-format signal in the far-end room was then decoded and played by the loudspeakers in the near-end room. Meanwhile, the echo signal was picked up by a microphone.