# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 4 Evaluation Results
### Comparisons in Complex Acoustic Scenes  
Although Scenarios I and II provide a degree of realism, they may not fully reflect real-world situations. To create a more challenging dataset that aligns with real-world conditions, we developed **Scenario III**, which is based on the U-Net-GCN model in [30]. The dataset comprised 65 h of audio with a sampling rate of 16 kHz. The training set included three rooms with dimensions of 3 \(\times\) 3 \(\times\) 2, 5 \(\times\) 4 \(\times\) 6, and 8 \(\times\) 9 \(\times\) 10 m, respectively; the development set comprised two rooms with dimensions of 5 \(\times\) 8 \(\times\) 3 and 4 \(\times\) 7 \(\times\) 8 m, respectively; and the test set included two rooms with dimensions of 4 \(\times\) 5 \(\times\) 3 and 6 \(\times\) 8 \(\times\) 5 m, respectively. All rooms had a reverberation time of 0.5 s (RT60 = 0.5 s). The sound source was located anywhere in the room, and RIRs were generated using the image method. Reverberant speech and noise were mixed at SNRs of \(-\)7.5, \(-\)5, 0, 5, and 7.5 dB. The training, validation, and test sets comprised 60 h, 4 h, and 1 h of audio, respectively.  
We report the in STOI, PESQ, and SDR results of both U-Net-GCN and CNABCFCN at different SNRs in Figure 9. The metrics for both networks improved as the SNR increased.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Mics**} & \multirow{2}{*}{**Para (M)**} & \multicolumn{3}{c}{**PESQ**} & \multicolumn{3}{c}{**STOI**} \\ \cline{3-10}  & & & \(\mathbf{-5}\)**dB** & \(\mathbf{0}\)**dB** & \(\mathbf{5}\)**dB** & \(\mathbf{-5}\)**dB** & \(\mathbf{0}\)**dB** & \(\mathbf{5}\)**dB** \\ \hline noisy & - & - & 1.37 & 1.67 & 1.98 & 66.91 & 75.57 & 83.26 \\ \hline C-TasNet & 1 & 5.1 & 2.39 & 2.75 & 3.02 & 81.38 & 88.95 & 93.17 \\ DCCRN & 1 & 5.3 & 2.23 & 2.63 & 2.95 & 80.79 & 89.02 & 92.58 \\ DPCRN & 1 & 0.8 & 2.36 & 2.67 & 2.91 & 75.08 & 81.60 & 86.28 \\ MNTFA & 1 & 0.23 & 2.42 & 2.72 & 3.02 & 75.64 & 81.97 & 86.78 \\ \hline CRN-i & 2 & 0.08 & 1.56 & 1.89 & 2.28 & 69.51 & 78.68 & 86.27 \\ CRN-ii & 2 & 17.6 & 1.61 & 1.96 & 2.33 & 71.06 & 79.85 & 87.15 \\ CNABCFCN & 2 & 9.2 & \(\mathbf{2.75}\) & \(\mathbf{3.07}\) & \(\mathbf{3.32}\) & \(\mathbf{89.22}\) & \(\mathbf{93.29}\) & \(\mathbf{95.74}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: PESQ and STOI of different networks on the DNS dataset (Scenario I).  
However, the proposed model outperformed U-Net-GCN, as demonstrated by, for example, the gains of 0.12, 0.56, and 3.74 in STOI, PESQ, and SDR, respectively, achieved by CNABCFCN over U-Net-GCN when SNR = 0 dB (as seen in Table 8).  
We compared the proposed CNABFCN model with the following dual-channel models in Scenario III to demonstrate its superior performance:  
1. DNN [31; 32], a vector-to-vector regression method that employs fully connected layers to perform speech enhancement.
2. TNN [33], a tensor-to-vector regression method used for multichannel speech enhancement.
3. BC-SE [34  
Table 9 presents the PESQ results of different networks. The PESQ results of DNN and TNN were averaged at input SNRs of 10 dB to 20 dB, while the results of BC-SE and CNABCFCN were computed at input SNRs of 5 dB to 15 dB. Despite the lower SNRs, the proposed network outperformed both DNN and TNN. Additionally, the proposed model outperformed BC-SE by 0.96, as shown in the table.