# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 6 Experimental Results and Discussion  
First, Table 1 displays the SI-SDRi scores obtained using SuDoRM-RF++ and the newly provided ESC-MASD-Net, which includes ResCon and MA blocks while retaining the original U-ConvBlock. Each of these networks is trained for 100 epochs. The table provides the following observations.  
1. The given ESC-MASD-Net outperforms SuDoRM-RF++ by 1.33 in SI-SDRi, demonstrating the success of SC-MASD-Net in speech separation.
2. Regarding an ablation study, the SI-SDRi decreases by 0.11 when the MA block is removed from ESC MA-SDNet, whereas it decreases by 0.54 when the ResCon block is removed from ESC-SDNet. These findings suggest that ESC-MASD-Net may benefit more from ResCon than MA in speech separation.
3. ESC-MASD-Net with MA alone and ESC-MASD-Net with ResCon alone perform better than SuDoRM-RF++. As a result, MA and ResCon both have the potential to improve SuDoRM-RF++'s separation behavior.  
Next, we evaluate alternative extensions of ESC-MASD-Net by inserting conformer layers into different positions of its four U-Convolutional blocks. Table 2 shows the outcomes of this evaluation. Our observations are as follows.  
1. The inclusion of the conformer layer to ESC-MASD-Net consistently increased the SI-SDRi score, regardless of where it was in the network. This demonstrates the utility of the conformer layer.
2. We obtained the best overall performance (SI-SDRi score of 13.95) when a single conformer layer was inserted directly before the first U-Convolutional block. Adding a conformer layer to the first U-Convolutional block (at the bottom, most downsampled portion) yielded an SI-SDRi score of 13.80. Adding a conformer layer to each of the four U-Convolutional blocks, on the other hand, resulted in an SI-SDRi score of 13.90, which was a 0.1 improvement over the previous example but required three additional conformer layers.  
Furthermore, we conduct experiments to see if increasing the number of training epochs could enhance the performance of ESC-MASD-Net. The original 100 epochs setting is found to be nearly optimal for SuDoRM-RF++, but it may not be the best option for ESC-MASD-Net. We list the SI-SDRi results for ESC-MASD-Net and its two variants that incorporate a conformer layer with optimal validation performance epochs in Table 3. By increasing the number of epochs from 100 to over 150 for ESC-MASD-Net and its two variants, we observe an SI-SDRi improvement of at least 0.20. As a result, ESC-MASD-Net significantly outperforms its archetype SuDoRM-RF++, with an SI-SDRi improvement from 12.38 to 14.24.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **SI-SDRi** \\ \hline SuDoRM-RF++ & 12.38 \\ \hline ESC-MASD-Net & 13.71 \\ \hline ESC-MASD-Net without MA block & 13.60 \\ \hline ESC-MASD-Net without ResCon block & 13.17 \\ \hline \end{tabular}
\end{table}
Table 1: The signal-to-distortion ratio improvement (SI-SDRi) obtained by various speech separation frameworks.  
Finally, Table 4 includes the model size and achieved SI-SDRi results of some state-of-the-art speech separation methods performed on the Wham! dataset. From this table, we see that our best model achieved the SI-SDRi score of 14.24 using 3.6M parameters. We also observed that our best model performs on par or even outperforms several state-of-the-art methods with much higher computational resource needs.