## 3 Model
### Results  
The average SI-SDR improvements (SI-SDRi, dB) of the three networks on the test dataset are shown in Table 2. In general, all networks achieved promising results, with DPTNet performing slightly better. Taking one mixture with SI-SDRi of about the average value as an example, spectrograms of the mixture, two clear sources (a female speaker and birds), and two estimated sources obtained by ConvTasNet with 32 sample window size are shown in Figure 2. The SI-SDR improvements of the two estimated sources are 9.39 dB and 12.92 dB, respectively. It can be seen that except for some fine components, most components are well separated and reconstructed. From the perspective of human hearing, these two sources are also well separated and with great intelligibility. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Description** & **ConvTasNet** & **DPRNN** & **DPTNet** \\ \hline Encoder/Decoder & Number of filters & 512 & 64 & 64 \\  & Window size of filters & 32/64/128 & 4/8/16 & 4/8/16 \\ \hline  & Number of repeated modules & 3 & 6 & 6 \\  & Number of channels in \(1\times 1\) conv bottleneck layer & 128 & 128 & N/A \\  & Number of stacked Dilated Conv-Blocks & 8 & N/A & N/A \\  & Number of channels in \(1\times 1\) conv layer of & 128 & N/A & N/A \\ Separator & skip-connection path & 128 & N/A & N/A \\  & Number of channels in Dilated Conv-Blocks & 512 & N/A & N/A \\  & Kernel size in Dilated Conv-Blocks & 3 & N/A & N/A \\  & Chunk size in segmentation & N/A & 250/150/100 & 250/150/100 \\  & Number of neurons in the RNNs cell state & N/A & 128 & 128 \\  & Number of attention heads & N/A & N/A & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters of three networks in our experiments, where “N/A” indicates this parameter is not used in this network. In addition to the average SI-SDRi in the entire dataset, the performances of three networks for mixtures with six different types are shown by spider charts with numerical labels in Figure 3. For the separation of speech and other sounds (speech and speech, speech and music, and speech and environmental sounds), the results of the three networks are all significantly better. It means that no matter what kind of network structure is adopted, they can separate sounds with clear structures such as speech. The limited separation between and within environmental sounds and music may be attributed to the following reasons: the music sound here refers to the music pieces rather than individual tones played by one instrument, consisting of a series of instrument tracks. If two music pieces with the same genre are mixed, it is difficult even for humans to separate them. Another reason is that the amount of conveyed information in environmental sounds may not be the same as that of speech, resulting in more difficulties in the separation. As an important parameter that affects network performance, results with different window sizes are also explored. For ConvTasNet, the window size is set to 32, 64, and 128 samples, corresponding to 2, 4, and 8 ms for 16 kHz sample rate, respectively. Due to the introduction of the dual-path processing, the window size is further decreased to two samples in DPRNN and DPTNet, which is extremely hard for traditional fully convolutional layers. Results show that regardless of network structures, separation \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Window Size (Samples)** & **SI-SDRi (dB)** \\ \hline \multirow{3}{*}{ConvTasNet} & 32 & 11.70 \\  & 64 & 10.89 \\  & 128 & 9.91 \\ \hline \multirow{3}{*}{DPRNN} & 4 & 12.14 \\  & 8 & 11.68 \\  & 16 & 11.41 \\ \hline \multirow{3}{*}{DPTNet} & 4 & 12.33 \\  & 8 & 12.01 \\ \cline{1-1}  & 16 & 10.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average SI-SDR improvement (dB) on the test dataset for different networks with different window sizes.