# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm Name** & **Algorithm References** & **Advantages** & **Disadvantages** \\ \hline Deep Neural Networks (DNN) & [52; 55; 64; 73; 76; 77; 80; 92; 96] & Adaptable to complex noise; data-driven; handles non-linear relationships. & Requires large datasets; computationally intensive; prone to overfitting. \\ \hline Convolutional Neural Networks (CNN) & [15; 86] & Effective for local feature extraction; scalable; translation-invariant. & Limited long-term context; struggles with temporal dynamics. \\ \hline RNN/LSTM & [54; 84; 94] & Captures sequential dependencies; handles long-term noise patterns. & Slow training; vanishing/exploding gradients; computationally heavy. \\ \hline Hybrid (RNN + CNN) & [56; 86] & Combines spatial (CNN) and temporal (RNN) modeling; robust for dynamic noise. & Complex integration; difficult to optimize; high resource demands. \\ \hline Wiener Filtering & [66; 71; 78; 85; 89; 90; 91; 95] & Mathematically simple; effective for stationary noise. & Assumes noise statistics are known; poor for non-stationary noise. \\ \hline MMSE Estimation & [1; 3; 68; 72; 79; 97] & Minimizes mean square error; statistically robust. & Computationally intensive; requires accurate noise estimation. \\ \hline Spectral Subtraction & [57; 67; 75] & Simple implementation; fast; historically proven. & Introduces “musical noise” artifacts; relies on accurate silence detection. \\ \hline Wavelet Denoising & [82] & Multi-resolution analysis; handles non-stationary noise. & Parameter-sensitive; limited effectiveness for high-frequency noise. \\ \hline Hybrid (Wiener + DNN) & [87; 93] & Combines model-based and data-driven approaches; improves generalization. & Requires careful hyperparameter tuning; increased complexity. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Main types of algorithms used in SEAs. **RQ2: What are the main types of transform-based SEA used in the existing studies? **  
The main kinds of transform-based SEA adopted by the reviewed findings are indicted in this section. It is worth mentioning that the processing of speech enhancement can be applied in the time domain and frequency domain. However, discrete transforms have been recognized as a very useful tool in signal processing such as speech enhancement. In this approach, speech is viewed in transform domains, resulting in a massive shift in terms of robust ability to analyze the components of speech signals [98]. Discrete transforms have different properties such as energy compaction and localization, which can be used to perform transform analysis in various practical applications. The significance of transforms in sequential data processing raises the possibility of using them in SEA research. Studies dedicated to SEAs include different types of transforms used in this field. As can be observed from Figure 8, the main types of transforms used in the reviewed papers are as follows: nine studies used the Short-Time Fourier Transform (STFT) [53; 60; 75; 78; 80; 84; 92; 94; 96]; five studies used the Fast Fourier Transform (FFT) [57; 66; 67; 90; 93]; and three studies utilized the Discrete Cosine Transform (DCT) [1; 72; 89]. In another technique presented by [3; 97], the authors used the Discrete Krawtchouk-Tchebichef Transform (DKTT), a powerful transform that has high energy compaction and localization properties, to handle speech signal coefficients. Finally, one paper was dedicated to the Discrete Fourier Transform (DFT) [56], another one used the Deep Complex Hybrid Transform (DCHT) [15], and [82] used the Wavelet Transform (WT). Additionally, some of the reviewed papers used more than one transform: FFT+DCT [86], STFT+FFT [71], DFT+STFT [61], DFT+STFT [51], DCT+DTCWPT+DWT [95], STFT+DCT [63], DFT+DCT+DWT [88], STFT+WSST (Wavelet Synchro-Squeezing Transform) [91], DFT+STFT [79], and STFT+FFT [87]. The main types of transforms used in SEAs are listed in Table 8 along with their features.