# Speech Enhancement Algorithms: A Systematic Literature Review
###### Abstract
Several studies have categorized SEAs based on the number of channels, distinguishing between single-channel and multi-channel approaches [12; 13]. Other studies have classified SEAs into three basic categories according to different processing techniques: spectral-subtractive algorithms, methods based on statistical models, and optimization criteria [6]. The latter category includes Wiener filtering (WF) [14], Minimum Mean Square Error (MMSE) estimation [9], deep learning algorithms [15]), and subspace algorithms [16]. A broad classification of SEAs can also be made based on processing domains, which can be divided into transform domain-based SEAs, time domain-based SEAs, and hybrid domain-based SEAs. Recently, there has been a growing trend toward speech enhancement models that combine the time and frequency (TF) domains [17; 18; 19]. In transform domain-based SEAs, degraded speech is processed in the transform domain, whereas temporal SEAs operate in the time domain. Each processing domain offers its own advantages and disadvantages [4]. Various discrete transforms have been used in SEAs, including the Discrete Fourier Transform (DFT) [9; 20], Wavelet Transform (WT) [21], Discrete Cosine Transform (DCT) [22], Discrete Krawtchouk Transform (DKT), and Discrete Tchebichef Transform (DTT) [14]. It is worth mentioning that different types of DCTs are utilized to improve speech quality, such as by reducing noise, extracting essential features, and enabling efficient compression. Each type has specific advantages that make it suitable for particular applications. DCT Type-II (DCT-II) [23; 24] is considered the most widely used type, especially in Mel-Frequency Cepstral Coefficients (MFCCs), which play a crucial role in speech recognition and enhancement. It is known for its excellent energy compaction, making it highly effective for speech compression and denoising. On the other hand,the DCT Type-III (DCT-III) [25], also referred to as Inverse DCT (IDCT), is essential for reconstructing speech signals after enhancement. It is often used in filtering bank-based speech processing to restore speech quality after noise suppression. Another important variant is DCT Type-IV (DCT-IV) [26], which is commonly applied in sub-band coding and speech denoising. It provides a smoother spectral representation of speech signals, helping to reduce artifacts and improve overall clarity. Additionally, the Modified Discrete Cosine Transform (MDCT) [27] is widely used for audio compression and real-time speech applications. Overall, these DCT-based techniques have greatly contributed to speech enhancement by improving noise reduction, compression efficiency, and speech intelligibility. Their continued relevance in modern deep learning-based SEAs highlights their lasting impact on the field. Each category of SEA can be implemented using specific transforms. Figure 1 illustrates the different classes of SEA based on dedicated processing domains [1]. SEAs have garnered significant attention due to their wide range of applications. Numerous reviews and surveys have been conducted to examine different aspects of SEAs. For example, ref. [28] investigated various techniques for speech de-reverberation and enhancement in noisy and reverberant environments. Two primary backbone Deep Neural Networks (DNNs) were compared, one operating in the time domain and the other in the frequency domain. In [29], the authors conducted a literature survey that discussed challenges such as nonstationary noise and overlapping speech. These are common issues in real-world scenarios, where speech signals are often degraded. A comprehensive summary of speech enhancement techniques and their various applications was presented in [29], focusing on traditional methods such as adaptive filtering and Wiener filtering as well as modern approaches such as deep learning-based techniques. Today, SEAs are integrated into various fields and applied in many practical applications, including telecommunications, hearing aids, speech recognition, and audio restoration.