# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
CNNs are a class of deep neural architecture consisting of one or more pairs of alternating convolutional and pooling layers [86]. In speech enhancement, long-term context is important for improving and understanding speech signals and processing continuous noise. RNNs can retain long-term context via modules such as LSTMs, making them better suited to handling long or complex signals. CNNs work well with short contexts, but require deeper networks or additional techniques such as dilated convolutions in order to simulate long-term effects. Therefore, RNNs were used in several of the reviewed studies. The SEA presented in [15] utilizes a CNN and a Fully-Connected Neural Network (FCNN). Another study used a novel LSTM-based speech preprocessor for speaker diarization in a realistic mismatch scenario [54]. To address the nonstationary of noise, one work used a Recurrent Neural Network (RNN)-based speech \begin{table}
\begin{tabular}{l c c} \hline \hline
**Indexing Platform** & **Number of Papers** & **Percentage of Total Papers (47)** \\ \hline Scopus & 42 & 89.4\% \\ \hline Web of Science & 39 & 83.0\% \\ \hline Emerging Web of Science & 8 & 17.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Indexing platforms for collected papers. Figure 7: Percentage of papers per source type. enhancement system to reduce wind noise [84], while another used an equilibriated RNN for real-time enhancement [94]. It is noteworthy that several studies were dedicated to hybrid speech enhancement algorithms. These dedicated hybrid forms of deep learning included RNN(LSTM)+CNN [56] and CNN+RNN [86] varieties. A different fundamental concepts using deep learning for speech processing was presented [86]. These methods show that deep learning is becoming more sophisticated and adaptable in solving speech enhancement problems. Another major approach is based on statistical models and optimization criteria, placing the problem of speech enhancement within a statistical and estimation framework. Among the retained studies, 18 out of 47 cover statistical approaches such as Wiener filtering (eight studies) [66; 71; 78; 85; 89; 90; 91; 95], MMSE estimation (six studies) [1; 3; 68; 72; 79; 97], or spectral subtraction (three studies) [57; 67; 75]. Spectral subtraction is a classical speech enhancement method that estimates the noise spectrum during speech pauses and subtracts it from the noisy spectrum to obtain the estimated clean signal. This process can also be performed by multiplying the noisy spectrum using the gain function and combining it with the phase of the degraded speech signal. The final study in this category uses a wavelet denoising approach [82]. Of the 47 reviewed papers, ten are dedicated to hybrid forms of speech enhancement: Wiener Deep Neural Network (WDNN) [87; 93]; MMSE+DL [60]; MMSE+Densely Connected Convolution Network (DCCN) [59]; DL+MMSE [61]; spectral subtraction with Wiener filter, CNN, and GNN [63]; hybrid Wiener filter for 1D-2D [88]; oblique projection and cepstral subtraction [69]; Mel-Frequency Cepstral Coefficients (MFCC)+DNN [83]; and WF generalized subspace+deep complex U-Net [70]. A summary of the main types of algorithms used in SEAs is provided in Table 7.