# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 5 Results and Analysis
### The Impact of \(\gamma\), \(\beta_{0}\) and \(\mu\)  
The testing results w.r.t. \(\gamma\), \(\beta_{0}\) and \(\mu\) are shown in Figure 2, where \(\gamma=1,2,3\), \(\beta_{0}=-10\) dB, \(-20\) dB, \(-30\) dB and \(\mu=0.5,1,2,3,4\) are considered. Here \(\alpha_{0}\) is set to 1 for all the conditions. The test results of three baselines are also presented as the comparison. From this figure, one can observe the following phenomena. First, the increase of \(\beta_{0}\) will decrease NA. This is because the residual noise control mechanism is introduced for optimization, which means, during the training process, the residual noise in the estimated spectra will gradually get close to the preset residual noise threshold. As a consequence, the characteristic of the residual noise is expected to be effectively preserved, which will be further confirmed by subjective listening tests in the following. Second, the increase of \(\mu\) is beneficial to noise suppression and meanwhile introducing more speech distortion. As generalized loss can be viewed as the joint optimization of both speech distortion and noise reduction, a larger \(\mu\) shows that more emphasis is given on noise suppression and it leads to smaller gain values, as (17) states, where on the one hand more interference is suppressed and on the other hand, more speech components are inevitably abandoned. Third, the increase of \(\gamma\) has a negative influence on NA and SA. In addition, when \(\gamma\) is set to 2, it shows a better objective speech quality than \(\gamma=1,3\). According to the above results, we have some general guidelines to choose the three parameters including \(\gamma\), \(\beta_{0}\) and \(\mu\). First, NA is expected to be as large as possible while SA needs to be as small as possible, indicating more noise components can be suppressed with less speech distortion. Second, both PESQ and SDR need to be as large as possible, indicating better speech quality is achieved. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Input Size** & **Hyperparameters** & **Output Size** \\ \hline reshape\_size\_1 & \(T\times 161\) & - & \(1\times T\times 161\) \\ conv2d\_1 & \(1\times T\times 161\) & \(2\times 3\), \((1,2)\), \(16\) & \(16\times T\times 80\) \\ conv2d\_2 & \(16\times T\times 80\) & \(2\times 3\), \((1,2)\), \(32\) & \(32\times T\times 39\) \\ conv2d\_3 & \(32\times T\times 39\) & \(2\times 3\), \((1,2)\), \(64\) & \(64\times T\times 19\) \\ conv2d\_4 & \(64\times T\times 19\) & \(2\times 3\), \((1,2)\), \(128\) & \(128\times T\times 9\) \\ conv2d\_5 & \(128\times T\times 9\) & \(2\times 3\), \((1,2)\), \(256\) & \(256\times T\times 4\) \\ deconv2d\_1 & \(256\times T\times 4\) & \(2\times 3\), \((1,2)\), \(128\) & \(128\times T\times 9\) \\ skip\_1 & \(128\times T\times 9\) & \(256\times T\times 9\) \\ deconv2d\_2 & \(256\times T\times 9\) & \(2\times 3\), \((1,2)\), \(64\) & \(64\times T\times 19\) \\ skip\_2 & \(64\times T\times 19\) & - & \(128\times T\times 19\) \\ deconv2d\_3 & \(128\times T\times 19\) & \(2\times 3\), \((1,2)\), \(32\) & \(32\times T\times 39\) \\ skip\_3 & \(32\times T\times 39\) & - & \(64\times T\times 39\) \\ deconv2d\_4 & \(64\times T\times 39\) & \(2\times 3\), \((1,2)\), \(16\) & \(16\times T\times 80\) \\ skip\_4 & \(16\times T\times 80\) & - & \(32\times T\times 80\) \\ deconv2d\_5 & \(32\times T\times 80\) & \(2\times 3\),\((1,2)\), \(1\) & \(1\times T\times 161\) \\ reshape\_size\_2 & \(1\times T\times 161\) & - & \(T\times 161\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed description of the netowrk used in the manuscript. The input size and output size of 3-D representation are given in \((Channels,TimeStep,Feat)\) format. The hyperparameters are specified with \((Kernel,Stride,Channels)\) format.