## 4 Experimental Results
### Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{4}{c}{**Speech Distortion Index**} \\ \cline{2-6}  & **(dB)** & **Noisy** & **MCRA** & **FB\_MCRA** & **Proposed** \\ \hline \multirow{3}{*}{Babble} & 0 & 0.3410 & 0.1706 & 0.1749 & 0.1598 \\  & 5 & 0.1078 & 0.0781 & 0.0764 & 0.0603 \\ \cline{1-1}  & 10 & 0.0341 & 0.0403 & 0.0343 & 0.0229 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._\((e_{T}^{MCRA})\). Therefore, the _Avg_SegSNR_ of the proposed method may be slightly better than the MCRA method. In the case of a noise-dominated region, the PSD of speech (\(E\{|S(\eta,\Omega)|_{2}\}\)) is less than that of background noise (\(E\{|D(\eta,\Omega)|_{2}\}\)), i.e., \(E\{|S(\eta,\Omega)|_{2}\}<E\{|D(\eta,\Omega)|_{2}\}\). Harmonics would be absent. The level of background noise is not underestimated in the proposed method. Thus the gain factor using the proposed noise estimator (\(g^{Proposed}(\eta,\Omega)\)) is comparable to that using the MCRA method (\(g^{MCRA}(\eta,\Omega)\)), i.e., \(g^{Proposed}(\eta,\Omega)\approx g^{MCRA}(\eta,\Omega)\). The _Avg_SegSNR of the proposed method is comparable to the MCRA method. Recently, deep learning based speech enhancement has become popular [22; 23; 24]. In [22], a deep auto-encoder (DAE) was proposed for speech denoising. This method trains the DAE by the features of noisy and speech pairs, enabling the DAE to learn the statistical difference between speech and noise, which helps to separate speech and noise for speech denoising. In [23], a SNR-based convolutional neural network (CNN) was proposed for speech denoising. This CNN can well deal with the local temporal-spectral structures of speech signals. In addition, the CNN is adapted by the SNR to improve denoising performance. [PERSON] et al. [24] proposed using deep neural networks (DNN) with a multiple-layer deep architecture for speech denoising. Large training features were utilized to train the DNN. The trained DNN plays the roles of nonlinear mapping from noisy speech features to clean speech features, enabling the acoustic context of denoised speech to be improved. By training the weighting and bias factors of the DNN using the feature pairs of noisy speech and clean speech, the DNN can capture the context information along the time axis by multiple frames expansion and along the frequency axis by log-spectral features with full frequency bins. The proposed noise estimator also can be further developed to incorporate with the DNN to capture the variation contour of noise power spectra for each frequency bin as a future work. Initially, speech utterances are interfered by various kinds of background noise to produce noisy speech for training the DNN. The log power spectra of noisy speech are employed as features to train the DNN model. In addition, the log power spectra of interference noise are also employed to train a DNN simultaneously. In the noise estimation phase, the log power spectra of noisy speech are computed and fed into the DNN. The mapping between the log power spectra of noisy speech and noise is performed by the trained DNN. Hence, by concatenating the output features of the noise DNN can obtain the power spectra of noise. Because speech components are absent in noise regions in an observed signal, the power spectra of the observed signal are more suitable to be the noise estimate. Accordingly, the noise estimator has to be adapted by the SNR, enabling the accuracy of noise estimation to be further improved.