# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 7 Conclusions  
The purpose of this study is to improve the state-of-the-art speech separation framework, SuDoRM-RF++, by incorporating it with a number of different convolution-based modules. These modules include residual conformer network, multi-attention network, and conformer layers. Having demonstrated that each additional module offers greater  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters (Millions)** & **SI-SDRi** \\ \hline Conv-TasNet [4] & 5.1 & 12.7 \\ \hline DPRNN [9] & 2.7 & 13.9 \\ \hline SuDoRM-RF2.5x [12] & 6.4 & 14.1 \\ \hline Sepformer [7] & 26 & 15.0 \\ \hline MossFormer [21] & 42.1 & 17.3 \\ \hline MossFormer2 [22] & 55.7 & 18.1 \\ \hline ESC-MASD-Net (Ours) & 3.6 & 14.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model size and performance comparison of the presented effective speaker separation through convolutional multi-view attention and SuDoRM-RF network (ESC-MASD-Net) with the other state-of-the-art speech separation models on WHAM!  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epoch** & **SI-SDRi** \\ \hline SuDoRM-RF++ & 100 & 12.38 \\ \hline ESC-MASD-Net & 100 & 13.71 \\ \cline{2-3}  & 153 & 13.94 \\ \hline ESC-MASD-Net with & (a) right before the & 100 & 13.95 \\ \cline{2-3}  & U-Convblocks & 151 & 14.24 \\ \cline{2-3}  & (b) at the bottom of the & 100 & 13.80 \\ \cline{2-3}  & first U-Convblock & 153 & 14.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The signal-to-distortion ratio improvement (SI-SDRi) obtained by effective speaker separation through convolutional multi-view attention and SuDoRM-RF network (ESC-MASD-Net) variants using different number of epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters (Millions)** & **SI-SDRi** \\ \hline Conv-TasNet [4] & 5.1 & 12.7 \\ \hline DPRNN [9] & 2.7 & 13.9 \\ \hline SuDoRM-RF2.5x [12] & 6.4 & 14.1 \\ \hline Sepformer [7] & 26 & 15.0 \\ \hline MossFormer [21] & 42.1 & 17.3 \\ \hline MossFormer2 [22] & 55.7 & 18.1 \\ \hline ESC-MASD-Net (Ours) & 3.6 & 14.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model size and performance comparison of the presented effective speaker separation through convolutional multi-view attention and SuDoRM-RF network (ESC-MASD-Net) with the other state-of-the-art speech separation models on WHAM!separation performance, we also show that these modules can work together to lead to even better outcomes than can be achieved by each module functioning independently. We will, in the future, research ways to lessen the complexity of the presented ESC-MASD-Net without compromising its performance.  
Conceptualization, A.N.A.; Methodology, A.N.A., C.-W.L. and J.-W.H.; Software, J.-W.H.; Validation, C.-W.L. and J.-W.H.; Investigation, A.N.A. and C.-W.L.; Data curation, A.N.A.; Writing--original draft, A.N.A. and J.-W.H.; Writing--review & editing, J.-W.H.; Visualization, C.-W.L.; Supervision, J.-W.H.; Project administration, J.-W.H.; Funding acquisition, J.-W.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that supports the findings of this study requires WSJ0 corpus and the WHAM noise corpus.The WSJ0 corpus which is a base corpus required to construct the wsj0-2 mix dataset is available for download at [[https://doi.org/10.35111/ewkm-cg47](https://doi.org/10.35111/ewkm-cg47)]([https://doi.org/10.35111/ewkm-cg47](https://doi.org/10.35111/ewkm-cg47)) upon request under a specific license agreement. The set of WHAM noise samples along with the scripts to build WHAM! dataset from wsj0-2 mix dataset are openly available to download at [[http://wham.whisper.ai/](http://wham.whisper.ai/)]([http://wham.whisper.ai/](http://wham.whisper.ai/)).  
The authors declare no conflicts of interest.