# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion  
The aim of this stage is to discuss and accurately record information from the candidate studies. To reduce the chance of bias, the data extraction process is clearly defined. Various types of algorithms are used for speech enhancement, in which multiple transforms and techniques have been implemented. To cover the trends in the candidate articles, an overview of these studies according to their year of publication is provided based on the 50 papers retained after the exclusion step. The year-wise distribution of the collected studies is displayed in Table 5. For more clarity and to provide a better understanding of trends in the SEA research area, the number of related research studies per year along with their percentage ratio is presented in Figure 6.  
Figure 7 shows the collected papers divided according to their types and sources, with the average provided for each type of publication. A final total of 47 studies were retained. As presented in Figure 7, 44.68% of the studies were published in journals, 46.81% were presented at conferences, and 8.51% were presented in workshops. Table 6 shows that the majority of the 47 papers collected for the full review were indexed in Scopus and Web of Science. Specifically, 42 papers (89.4%) were indexed in Scopus and 39 papers (83.0%) in the Web of Science database, with a smaller subset of eight papers (17.0%) indexed in the Emerging Web of Science. This indicates that the majority of the collected literature is well-represented in established indexing platforms, which ensures a robust foundation for our systematic review. Overall, the dataset reflects a strong emphasis on credible and widely recognized sources. In this section, the research questions are answered in detail to achieve the objectives accurately and clearly. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** & **2022** & **2023** & **2024** \\ \hline
[84] & [66] & [55] & [53] & [51] & [74] & [56] & [52] & [65] & [62] \\
[87] & [67] & [69] & [54] & [60] & [94] & [97] & [68] & [72] & [63] \\
[88] & [71] & [79] & [57] & [3] & [95] & [59] & [73] & [76] & [70] \\
[78] & [1] & [58] & [96] & & & & & [80] & [77] \\
[85] & [84] & [64] & [61] & & & & & [82] & \\
[89] & [90] & [93] & & & & & & [15] & \\  & [91] & [81] & & & & & & [83] & \\  & [92] & & & & & & & [86] & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Year-wise breakup of the collected studies. Figure 6: Distribution of research trends related to SEA from 2015 to 2024. **RQ1. What are the most commonly used recent research approaches in the field of SEAs? **  
Speech enhancement processes aim to recovers the desired clean speech of the damaged signal, with different approaches depending on the type of degradation and noise signal. Practically, this process remains challenging, specifically when dealing with high noise levels, nonstationary noise, and reverberation. Therefore, multiple approaches have emerged in recent years. Different types of SEA have been are adopted in the reviewed papers. It is worth noting that deep learning has recently attracted the attention of many researchers. The reviewed studies on SEAs investigate varied topics, including statistical based approaches, deep learning-based approaches, and hybrid methods that incorporate different approaches such as conventional speech enhancement and deep learning techniques. Specifically, topics such as phase-sensitive masks, harmonic regeneration, and time-frequency mask estimation have become popular. Hybrid approaches combining techniques such as Wiener filtering with neural networks are commonly used to improve voice intelligibility and lower noise in complex situations such as mobile communication, hearing aids, and drones. Among the 47 reviewed studies, several cover deep learning [51; 53]. Nine of the retained studies were dedicated to DNNs [52; 55; 64; 73; 76; 77; 80; 92; 96], while other studies presented CNNs for use in SEAs.