# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 5 Result and Analysis
### Actual Effects and Comparison with Advanced Systems  
Figure 5 compares the actual effects of speech enhancement between the baseline (FullSubNet) and FullSubNet-pMix using spectrograms. In the figure, (a) represents the noisy speech with reverberation; (b) represents the noisy speech without reverberation, where the added noise is the same as in (a); (c) represents the result of baseline speech enhancement with reverberation; (d) represents the result of baseline speech enhancement without reverberation; (e) represents the result of speech enhancement using the proposed method without reverberation. The green boxes in the figure indicate the specific differences between the baseline and the proposed method after speech enhancement. From the figure, it can be observed that, regardless of the presence of reverberation, FullSubNet-pMix achieves better speech enhancement results compared to the baseline. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Num** & **Model** & **Magphase** & **Lookahead (ms)** & **Sb\_num** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** \\ \hline
1 & FullSubNet & 0 & 32 & 15 & 2.782 & 3.268 & 95.83 & 17.12 \\
2 & FullSubNet-pMix & 3 & 32 & 15 & 2.835 & 3.329 & 96.07 & 17.49 \\
3 & FullSubNet & 0 & 32 & 10 & 2.630 & 3.149 & 95.32 & 16.69 \\
4 & FullSubNet-pMix & 3 & 32 & 10 & 2.699 & 3.225 & 95.54 & 16.96 \\
5 & FullSubNet & 0 & 2 & 10 & 2.792 & 3.276 & 96.05 & 17.30 \\
6 & FullSubNet-pMix & 3 & 2 & 10 & 2.805 & 3.288 & 95.88 & 17.32 \\
7 & FullSubNet-pMix & 1 & 2 & 10 & 2.798 & 3.277 & 95.92 & 17.27 \\
8 & FullSubNet-pMix & 2 & 2 & 10 & 2.771 & 3.252 & 95.82 & 17.14 \\
10 & FullSubNet-pMix & 4 & 2 & 10 & 2.866 & 3.342 & 96.24 & 17.66 \\
11 & FullSubNet-pMix & 5 & 2 & 10 & 2.819 & 3.317 & 96.15 & 17.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effect of Magphase units on speech enhancement capabilities. Figure 5: Comparison of the actual effect of voice enhancement. (**a**) represents the noisy speech with reverberation; (**b**) represents the noisy speech without reverberation; (**c**) represents the result of baseline speech enhancement with reverberation; (**d**) represents the result of baseline speech enhancement without reverberation; (**e**) represents the result of speech enhancement using the proposed method with reverberation; (**f**) represents the result of speech enhancement using the proposed method without reverberation. The experimental results were compared with other models on the Interspeech 2021 test dataset. These models were selected from previous participating models in the Interspeech speech enhancement competition, and corresponding papers were published. Since some of these models are not open source, the data used in the table are obtained from their respective papers. The DCCRN-E model [4] utilizes a five-layer convolutional encoder to extract high-level features from the input. The PoCoNet model [22] employs self-attention mechanisms to capture temporal information. The TRU-NET model [23] uses energy-normalized spectrum as input features for each channel. For the results obtained by the proposed models in the table, Table 3 corresponds to the evaluation of speech enhancement results in the presence of reverberation in the noisy speech, while Table 4 represents the case without reverberation. FullSubNet refers to the baseline model with Lookahead hyperparameters of 32 and a sub-band width of 15. FullSubNet-pMix represents the proposed method in this paper, with 4 Magphase units, a Lookahead of 2, and a sub-band width of 10. From the table, it can be observed that, for all four evaluation metrics, the proposed method outperforms other recent speech enhancement models in the presence of reverberation. In the absence of reverberation, except for a slight inferiority compared to TRU-NET, the FullSubNet-pMix model shows better speech enhancement performance compared to other models. Regardless of the presence of reverberation, the results show improvement compared to the baseline, demonstrating the effectiveness of the proposed method.