# 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 Hybrid (MMSE + DCCN) & [59; 61] & Balances error minimization and deep feature learning; robust performance. & High computational cost; integration challenges. \\ \hline Hybrid (Spectral Subtraction + CNN/GNN) & [63] & Leverages traditional and deep learning methods; flexible. & Complex implementation; risk of over-smoothing. \\ \hline Hybrid (1D-2D Wiener Filter) & [88] & Handles multi-dimensional noise; adaptable to varied scenarios. & Limited validation in dynamic environments. \\ \hline Oblique Projection + Cepstral Subtraction & [69] & Reduces reverberation; preserves speech quality. & Computationally demanding; niche applicability. \\ \hline MFCC + DNN & [83] & Leverages spectral features (MFCC) with deep learning; improves intelligibility. & Depends on MFCC accuracy; limited to spectral domain. \\ \hline Deep Complex U-net & [70] & Processes complex spectral data; effective for phase-sensitive tasks. & High memory usage; requires specialized training. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cont. \begin{table}
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**Transform** & **Transform References** & **Key Features** \\ \hline Short-Time Fourier Transform (STFT) & [53; 60; 75; 78; 80; 84; 92; 94; 96] & Time-frequency analysis using overlapping windows; effective for non-stationary signals. \\ \hline Fast Fourier Transform (FFT) & [57; 66; 67; 90; 93] & Computationally efficient spectral analysis; widely used for frequency-domain processing. \\ \hline Discrete Cosine Transform (DCT) & [1; 72; 89] & High energy compaction; useful for compression and noise reduction. \\ \hline Discrete Krawtchouk-Tchebichef Transform (DKTT) & [3; 97] & High energy localization; robust for speech signal coefficient analysis. \\ \hline Discrete Fourier Transform (DFT) & [56] & Fundamental frequency-domain analysis; represents signals via sinusoidal components. \\ \hline Deep Complex Hybrid Transform (DCHT) & [15] & Combines complex transforms with deep learning for hybrid time-frequency processing. \\ \hline Wavelet Transform (WT) & [82] & Multi-resolution analysis; effective for non-stationary noise and transient signals. \\ \hline FFT + DCT & [86] & Combines FFT’s speed with DCT’s energy compaction for enhanced signal analysis. \\ \hline STFT + FFT & [71; 87] & Integrates time-frequency (STFT) and spectral (FFT) analysis for advanced processing. \\ \hline DFT + STFT & [51; 61; 79] & Merges DFT’s spectral precision with STFT’s time-frequency resolution. \\ \hline DCT + DTCWPT + DWT & [95] & Combines DCT, Dual-Tree Complex Wavelet Packet Transform (DTCWPT), and Discrete Wavelet Transform (DWT) for multi-level analysis. \\ \hline STFT + DCT & [63] & Links STFT’s time-frequency analysis with DCT’s energy compaction for feature extraction. \\ \hline DFT + DCT + DWT & [88] & Hybrid approach for multi-domain analysis of complex signals. \\ \hline STFT + WSST & [91] & Combines STFT with Wavelet Synchro Squeezing Transform (WSST) for enhanced time-frequency resolution. \\ \hline \end{tabular}
\end{table} 
Table 8: Main types of transforms used in SEAs. **RQ3: What is the number of channels presented in the current works? **  
The reviewed papers presented several main approaches that improve the performance of SEA in noisy environments based on the channel number. To answer this question, we focused on selected SEA studies that examined single channel-based speech enhancement and multi-channel-based speech enhancement. Single-channel SEA uses a single-channel speech signals; in this approach, signals can be collected by a single microphone. On the other hand, multi-channel SEA works on multiple channel signals, which helps to improve the degraded signal. The single-channel scenario is more common in real situations [80].