## 1 Introduction  
Speech processing technology has been successfully integrated into many networking and multimedia audio-visual services, such as voice input of smart devices, interactive voice chat, voice command control of various intelligent robots, and voice control of automobile and locomotive functions. However, there are still many critical challenges in expanding speech applications. From the perspective of signal processing, the primary problem facing speech signal transmission and processing is noise interference. As described in the best-selling book \"A Flaw in Human Judgment\" [1] in 2021, bias and noise are two major sources of error. However, compared to bias, the randomness of noise makes it much less likely to predict accurately, thus deepening the difficulty of dealing with noise, while this is primarily a narrative of human judgment, it seems equally applicable to the environment that speech signal processing inhabits. Thanks to the rapid development of science and technology, tremendous countermeasures have been presented in recent decades to deal with the distortion existing in communicated speech. One of the prevailing schools is speech enhancement (SE), which aims to improve the quality and intelligibility of received speech so that the enhanced utterances can be more readily accepted and understood by human beings or machines. Conventional SE algorithms mainly rely on statistical modeling of speech or noise, and they often perform unexpectedly well in non-stationary noise scenarios. They include spectral subtraction [2], the Weiner filtering method [3], maximum a posteriori adaptation (MAP) [4], maximum likelihood linear regression (MLLR) [5], maximum likelihood linear transform (MLLT) [6], parallel model combination (PMC) [7], minimum mean square error log-spectral amplitude estimator (MMSE-LSA) [8], stochastic vector mapping (SVM) [9], and multi-environment model-based linear normalization [10], just to name a few. In recent years, thanks to the development of the theory and application of deep neural networks (DNN), most state-of-the-art SE technologies developed by mainstream researchers contain deep-learning algorithms. These DNN-wise techniques allow the extensive use of speech data to train SE models, exhibiting more excellent SE performance with greater generalizability than conventional methods. In short, in the supervised DNN-based SE methods, a large amount of paired training data (viz., noisy speech and the corresponding clean speech component) is used to train a DNN. The DNN is to learn a mapping between noisy and clean utterances to estimate the clean speech component of any noisy speech accurately. Furthermore, the performance of DNN-based SE methods can be determined by three factors: the model architecture, the data used for learning, and the employed hyper-parameters. As for the model architecture, various types of deep neural networks, such as multi-layer perception (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), denoising auto-encoder (DAE), and the corresponding variants and evolutions have been adopted to develop SE methods. For example, the works in [11; 12; 13; 14; 15] use MLP architectures, the works in [16; 17; 18; 19; 20] employ a CNN framework, and RNN is exploited in [21; 22]. Furthermore, the training target of network learning is also significantly related to the performance of the SE methods. According to [23], the SE methods can be divided into two categories due to the training target: mapping-based and masking-based. Mapping-based methods directly pursue a mapping function with the ideal output being the representation (feature) of the clean speech component for the noisy input. The representation might be time-domain signal waveform and time-frequency (T-F) diagrams including spectrograms and cochleagrams. Some well-known mapping-based SE methods include, but are not limited to, the target magnitude spectrum (TMS) [24], gammatone frequency target power spectrum (GF-TPS) [24], and signal approximation (SA) [24].