## 5 Concluding Remarks  
In this study, we primarily pay attention to the noise issue in speech signals, discuss some celebrated deep-learning-based SE methods, and propose the use of the DWT to create features for an encoder-decoder SE framework. One-level and two-level DWT features are extracted and then complemented by time-domain features for SE model learning. We extend the concept of bi-projection fusion (BPF) and provide a novel fusion structure, multiple-projection fusion (MPF), that can integrate three (or more) feature sources adaptively. We provide two types of MPF for two-level DWT features, which serve as intra-channel softmax and inter-channel softmax, respectively.  
With the well-known VoiceBank-DEMAND and VoiceBank-QUT tasks, we evaluate two SE architectures, Conv-TasNet and DPTNet, with various fusions of time-domain and DWT-domain features. Preliminary results show that DWT-domain features compensate for time-domain features to produce higher SE performance, particularly the PESQ metric score. Although the fusion of DWT-domain and time-domain features does not always result in a statistically significant improvement in PESQ for Conv-TasNet when compared to pure time-domain features, it consistently performs significantly better for DPTNet, an SE framework more advanced than Conv-TasNet. It is worth noting that the presented feature sets offer DPTNet with excellent SE performance for the VoiceBank-QUT task, which includes unseen and non-stationary noises.  
Figure 15: Spectrogram of the noisy sentence in Figure 14b enhanced by (**a**) time-feature DPTNet, (**b**) one-level DWT-wise DPTNet with one BPF and concatenation, two-level DWT-wise DPTNet with (**c**) two BPFs, and (**d**) one MPF with inter-channel softmax. The regions outlined by the red box help us compare different methods in denoising.  
In particular, DWT features work well as a substitute for STFT features as the encoder's fusion component to deliver comparable or superior SE results. Since we conduct the convolution using a small-support wavelet function (db2), DWT features may be produced more quickly than STFT features, making them a competitive feature type for learning the DNN-based SE framework.  
As future work, we will further investigate whether the type of wavelet function used in the creation of DWT features influences the performance of the involved SE framework. Next, we will test other types of fusions for time-domain and DWT-domain features to see their SE behavior. Finally, to examine their portability, we plan to employ DWT features in other recently proposed SE frameworks, such as DEMUCS [49] and MANNER [50].  
Methodology, Y.-T.C. and J.-W.H.; Validation, Y.-T.C., Z.-T.W. and J.-W.H.; Investigation, Z.-T.W.; Resources, J.-W.H.; Data curation, Z.-T.W.; Writing--original draft, Y.-T.C.; Writing--review & editing, J.-W.H.; Supervision, J.-W.H.; Project administration, J.-W.H.; Funding acquisition, J.-W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council (grant number 111-2221-E-260-005-MY2).  
Not applicable.  
The authors declare no conflict of interest.