# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 3 Experiments and Results
### Performance Evaluation of Branchy Neural Network
#### 3.2.2 Denoising Performance Evaluation  
The noise classification guided denoising model with four special branch paths only (denoted as CGBNN-4) and the denoising model with four special branches and one common branch (denoted as CGBNN-5) were tested for performance evaluation in the seen and unseen noise cases of the test dataset. Each special branch is designed to suppress a specific type of noise interference, and the common branch is designed for the suppression of all four noises. That is, unlike CGBNN-4, which only considers the dedicated system for noise reduction, CGBNN-5 adopts both the dedicated system and the general system for noise reduction.  
To further evaluate the model superiority of our proposed branchy neural network, we compared the performance results of PESQ and STOI between two branchy methods and two DNN methods without environmental noise information. The classical fully connected DNN model [13] with three hidden layers and the state-of-the-art method [18] using LSTM for progressive learning are denoted as \"DNN baseline\" and \"LSTM-PL\", respectively, as shown in Figure 2. The unprocessed noisy speech (denoted as \"Noisy\") is also presented in Figure 2 to show how much performance has been improved. In addition, Figure 2 presents the denoising performance results of four DNN-based methods in four seen and two unseen noise environments. Figure 2a,b shows the averaged PESQ results in the seen and unseen noise environments. These results demonstrate that the CGBNN-4 and CGBNN-5 achieved better quality of reconstructed speech than the classical DNN baseline and LSTM-PL method. The strategy of combining expert systems (special branch) with an omnipotent system (common branch) enables the CGBNN-5 to have the best denoising performance and generalization ability. Compared with the LSTM-PL and DNN-baseline method, the proposed CGBNN-5 achieved an averaged PESQ improvement of 7.24% and 4.26% in seen noise cases and 3.94% and 5.32% in unseen noises, respectively. For the speech intelligibility evaluation, Figure 2c,d shows the averaged STOI results in the seen and unseen noise environments. The proposed CGBNN-5 achieved an averaged STOI improvement of 2.26% and 2.68% over the DNN-baseline in seen and unseen noise cases, respectively. Although the proposed CGBNN-5 does not perform better than the LSTM-PL method, the CGBNN-5 has lower computational complexity than the LSTM-PL. The feed-forward operation saves more model parameters than the recursive operation of LSTM networks. Furthermore, the proposed CGBNN-5 did not achieve improvement for the STOI measurement in a high SNR condition (15 dB), but it still made contributions on the improvement of PESQ in the 15 dB SNR case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classification Accuracy (\%)**} & \multicolumn{2}{c}{**Input Features**} \\ \cline{2-3}  & **Noisy LPS** & **Noisy LPS + Noise LPS** \\ \hline Train dataset & 97.41 & 99.92 \\ Validation dataset & 94.55 & 99.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy results in train and validation datasets.