## 4 Experimental Setup, Results, and Discussion
### Experiments on DPTNet with Two Tasks  
As introduced in Section 2.2, the DPTNet method exploits a more delicate mask-estimation module than Conv-TasNet, and it is supposed to provide superior SE performance. Therefore, we further evaluate some encoding features used in the previous two sections in the DPTNet framework with the two benchmark tasks: VoiceBank-DEMAND and VoiceBank-QUT. The corresponding evaluation results are listed in Tables 3 and 4. Observing these two tables and comparing them with the previous two tables for Conv-TasNet, we present the following findings:  
1. Almost all encoding features provide better metric scores in DPTNet than in Conv-TasNet as expected, except for the time-domain features. In our opinion, the improper model configuration might cause this performance disagreement in the case of time features.
2. When integrating with time features, STFT and various forms of DWT features provide DPTNet with similar STOI and SI-SNR results.
3. The PESQ results for DPTNet are somehow converse to those for Conv-TasNet. With the DPTNet framework, STFT features behave better in PESQ than DWT features for the VoiceBank-DEMAND task, while they are inferior for the VoiceBank-QUT task. However, as for the VoiceBank-DEMAND task, the DWT features with the integration \"one MPF with inter-channel softmax\" provides PESQ close to the STFT features. In contrast, the DWT features with the integration \"one BPF and concatenation\" behave significantly better than the STFT features in PESQ for the more challenging VoiceBank-QUT task.
4. The results with different feature sets are not entirely consistent for DPTNet and Conv-TasNet. However, we can conclude that the presented DWT-domain features offer beneficial SE information. They are additive to the time-domain features to improve the performance of both Conv-TasNet and DPTNet. In addition, referring to Tables 15 and 16, we find that all of the three fusion types (\"one BPF and concatenation\", \"two BPFs\", \"one MPF with inter-channel softmax\") listed here can benefit DPTNet with statistically significant improvement in PESQ relative to DPTNet us  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**VoiceBank-QUT**} \\ \cline{3-5}
**Feature Domains** & **Integration Manner** & \multicolumn{3}{c}{**Metric Scores**} \\ \cline{3-5}  & & **PESQ** & **STOI** & **SI-SNR** \\ \hline \multicolumn{5}{c}{Unprocessed baseline} & 1.247 & 0.784 & 3.876 \\ \hline Time & - & 1.908 & 0.860 & 13.694 \\ \hline Time and STFT [31] & - & 1.936 & 0.863 & 13.779 \\ \hline \multirow{3}{*}{Time and one-level DWT} & Addition & 1.922 & 0.858 & 13.645 \\ \cline{2-5}  & Concatenation & 1.932 & 0.861 & 13.775 \\ \cline{2-5}  & One BPF and concatenation & 1.936 & 0.862 & 13.824 \\ \hline \multirow{3}{*}{Time and two-level DWT (concatenation)} & Two BPFs & 1.922 & 0.861 & 13.837 \\ \cline{2-5}  & One MPF with intra-channel softmax & 1.917 & 0.861 & 13.748 \\ \cline{1-1} \cline{2-5}  & One MPF with inter-channel softmax & 1.926 & 0.859 & 13.729 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The evaluation results of Conv-TasNet using different domains of features for the test set in noise environment of “QUT-NOISE”.  
ing the time-domain feature only. These results indicate that DWT-domain features improve DPTNet more than Conv-TasNet in both DEMAND and QUT noise scenarios.