# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 4 Experiments and Discussion
We split \(80\%\) of the dataset into a training set, and the remaining \(20\%\) forms the test set. That is, \(120\),\(000\) pairs of the three datasets per SNR are used to train the same SE model, with the remaining pairs used for testing. Additionally, we employ the CSTR VCTK Corpus [25] and Demand database [26] as the original pure speech and noise datasets for the recordings because these datasets are publicly available. Finally, we adopt the SEGAN [5] model as the SE model for training and testing because its code is publicly available and is widely used in current research. To compare the quality of the speech enhancement, we use the following objective evaluation metrics:  
PESQ [27]: Perceptual evaluation of speech quality (from \(-0.5\) to \(4.5\)). CSIG [28]: MOS prediction of the signal distortion (from \(1\) to \(5\)). CBAK [28]: MOS prediction of the background noise intrusiveness (from \(1\) to \(5\)). COVL [28]: MOS prediction of the overall effect (from \(1\) to \(5\)). SNR [27]: Signal-to-Noise Ratio (from \(0\) to \(\infty\)). In our experiments, the results are calculated by comparing the enhanced signal against the pure signal. In order to maximize the accuracy of the experimental results and reduce any possible errors, all metrics are based on an average of \(30\) groups of speech signals. Table 1 presents the results for the first \"GAN-train\" and \"GAN-test\" experimental group, while Table 2 shows the corresponding results of the second group. From Tables 1 and 2, we observe the following: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Metric**} & \multirow{2}{*}{**PESQ**} & \multirow{2}{*}{**CSIG**} & \multirow{2}{*}{**CBAK**} & \multirow{2}{*}{**COVL**} & \multirow{2}{*}{**SNR**} \\ \cline{3-3} \cline{6-7} \multicolumn{1}{c}{**Trained Model/SNR/Test on**} & & & & & & & \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} GAN-train \\ (SE trained on our synthetic \\ training set) \\ \end{tabular} } & \multirow{2}{*}{\(-5\) dB} & Synthetic noisy by NM-GAN & \(1.35\) & \(2.81\) & \(2.90\) & \(2.80\) & \(3.67\) \\ \cline{2-7}  & & & Real noisy & \(1.09\) & \(2.76\) & \(2.72\) & \(2.29\) & \(3.11\) \\ \cline{2-7}  & & & Synthetic noisy by NM-GAN & \(2.52\) & \(3.15\) & \(3.27\) & \(2.93\) & \(8.15\) \\ \cline{2-7}  & & Real noisy & \(2.32\) & \(3.15\) & \(3.21\) & \(2.63\) & \(7.94\) \\ \cline{2-7}  & & Synthetic noisy by NM-GAN & \(2.60\) & \(3.79\) & \(3.72\) & \(3.25\) & \(7.12\) \\ \cline{2-7}  & & Real noisy & \(2.45\) & \(3.56\) & \(3.47\) & \(3.16\) & \(6.88\) \\ \hline \multirow{6}{*}{
\begin{tabular}{c} GAN-test \\ (SE trained on real training set) \\ \end{tabular} } & \multirow{2}{*}{\(-5\) dB} & Synthetic noisy by NM-GAN & \(1.01\) & \(2.26\) & \(2.32\) & \(2.19\) & \(3.07\) \\ \cline{2-7}  & & Real noisy & \(1.04\) & \(2.32\) & \(2.41\) & \(2.25\) & \(3.18\) \\ \cline{2-7}  & & Synthetic noisy by NM-GAN & \(2.15\) & \(2.85\) & \(3.18\) & \(2.14\) & \(7.36\) \\ \cline{2-7}  & & Real noisy & \(2.21\) & \(3.04\) & \(3.17\) & \(2.21\) & \(7.40\) \\ \cline{2-7}  & & Synthetic noisy by NM-GAN & \(2.03\) & \(3.12\) & \(3.23\) & \(3.23\) & \(7.82\) \\ \cline{2-7}  & & Real noisy & \(2.05\) & \(3.17\) & \(3.29\) & \(3.37\) & \(8.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation results for the first group of “GAN-train” and “GAN-test” experiments. (1) The \"GAN-train\" results in Table 1 reveal that the PESQ differences are overall 0.2. For the CSIG, CBAK, and COVL metrics, the average differences are 0.09, 0.16, and 0.3, respectively. With regard to the SNR improvement, the performance differences are 0.56, 0.21 and 0.24, for \(-\)5 dB, 0 dB and 5 dB, respectively. Considering all these results, we conclude that the SE model trained on the NM-GAN synthetic training sets provides appealing noise reduction levels for real noisy speech signals. Note that the noise and speech are synthesized through direct addition, so the noisy synthetic speech has some differences from the real noisy speech.