# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Comparison with Other Models  
We compare the proposed model with other models on two datasets using the experimental results provided in the original papers. On the DNS Challenge dataset, as seen in Table 4, our model improved PESQ without significantly increasing the number of model parameters and maintained an advanced level in other objective metrics. On the Voicebank + DEMAND dataset, as shown in Table 5, except for slight inferiority to DEMUCS [30] in CSIG, our model similarly shows significant improvements in all metrics. Compared to other studies, the advantage of our proposed method lies in considering the crucial role of harmonic information under noise masking for speech restoration. When estimating the phase spectrum, we follow the complex spectrum calculation method separately from the real and imaginary parts. We achieve collaborative optimization of the phase spectrum and magnitude spectrum using multiple losses while also considering contextual information in the speech.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **PESQ** & **STOI (\%)** & **SI-SDR** & **CSIG** & **\#Params. (M)** \\ \hline
2020 DCCRN [10] & 2.711 & 96.0 & 17.967 & 2.98 * & 3.67 \\
2020 DEMUCS [32] & 2.20 & 82.0 & 15.56 & 3.44 * & 33.5 \\
2022 Fullsubnet-plus [33] & 2.487 & 79.6 & 18.34 & 3.12 * & 8.6 \\
2022 TaylorSENet [12] & 3.23 & 97.69 & 19.78 & 3.49 * & 5.4 \\
2023 MP-SENet [34] & 3.50 & **98.00** & 20.31 & **4.73** * & 13.24 * \\ Ours **HRLF-Net** & **3.513** & 96.68 & **20.36** & 4.22 * & 13.105 \\ \hline \hline \end{tabular}  
* indicates that the corresponding result is not provided in the original paper, and the value in this table is obtained through our experiments.  
\end{table}
Table 4: Comparison with other models on the DNS Challenge 2020 dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Para. (M)** & **PESQ** & **STOI (\%)** & **SI-SDR** \\ \hline
**HRLF-Net** & 13.105 & 3.513 & 96.68 & 20.36 \\ -Fading-out & 12.245 & 3.229 & 96.26 & 17.50 \\ -Harmonic repair & 9.535 & 3.326 & 95.88 & 16.38 \\ -skimLSTM & 7.630 & 3.175 & 95.24 & 15.92 \\ +LSTM & 9.36 & 3.198 & 95.56 & 16.42 \\ -FFC & 12.805 & 3.372 & 95.22 & 17.15 \\ +Conv2D (origin) & 13.065 & 3.397 & 96.13 & 17.39 \\ \hline \hline \end{tabular}  
* indicates that the corresponding result is not provided in the original paper, and the value in this table is obtained through our experiments.  
\end{table}
Table 2: Ablation experiments targeting the main submodules on the DNS Challenge 2020 dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Para. (M)** & **PESQ** & **STOI (\%)** & **SI-SDR** \\ \hline
**HRLF-Net** & 13.105 & 3.519 & 96.78 & 18.67 \\ -Fading-out & 12.245 & 3.329 & 96.36 & 17.50 \\ -Harmonic repair & 9.535 & 3.426 & 95.98 & 16.38 \\ -skimLSTM & 7.630 & 3.275 & 95.34 & 15.92 \\ +LSTM & 9.36 & 3.298 & 95.66 & 16.42 \\ -FFC & 12.805 & 3.472 & 95.32 & 17.15 \\ +Conv2D (origin) & 13.065 & 3.477 & 96.23 & 17.39 \\ \hline \hline \end{tabular}  
* To serve as a control, the effectiveness of FFC is studied using conventional 2D convolution substitution, where +/- indicates whether the submodule is masked in the experiments.  
\end{table}
Table 3: Ablation experiments targeting the main submodules on the VoiceBank + DEMAND dataset.