# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 5 Conclusions  
This study evaluates a 6-level U-Net constructed with either CCABs or GLFBs to assess the efficacy of DNN-based speech denoising across various domains. To ensure causality, the U-Net employs a frame-buffering mechanism that collects feature sequences from the current and previous seven frames. Our experimental results demonstrate consistent enhancements in CSIG, CBAK, COVL, and PESQ for U-Nets operating in the STFT and STDCT domains, outperforming those in the temporal domain. Importantly, the U-Net built with GLFBs features fewer learnable parameters and enhanced denoising efficiency. Given the compatibility of STDCT and STFT with perceptual-based loss functions, we explored domain-specific composite loss functions to improve perceptual quality further. Notable improvements in PESQ and STOI scores were observed when accounting for factors like power compression and the trade-off between spectral magnitudes and phases.  
In future work, we plan to expand the capabilities of our denoising DNN based on these findings. Although the proposed U-Net significantly enhances speech quality, the output still remains at 8 kHz narrowband. Our next objective is integrating super-resolution techniques into the denoising DNN to obtain high-quality 32- or even 48-kHz wideband speech.  
Conceptualization, H.-T.H.; data curation, H.-T.H. and T.-T.L.; formal analysis, H.-T.H.; funding acquisition, H.-T.H.; investigation, H.-T.H. and T.-T.L.; project administration, H.-T.H.; resources, H.-T.H. and T.-T.L.; software, H.-T.H. and T.-T.L.; supervision, H.-T.H.; validation, H.-T.H.; visualization, H.-T.H.; writing--original draft, H.-T.H.; writing--review and editing, H.-T.H. and T.-T.L. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Science and Technology Council, Taiwan (R.O.C.) under Grants NSTC 112-2221-E-197-026 and 113-2221-E-197-024.  
**Data Availability Statement:** The speech and noise datasets analyzed during this study are accessible from the CSTR VCTK Corpus [[[https://www.kaggle.com/datasets/muhmagdy/valentini-noisy](https://www.kaggle.com/datasets/muhmagdy/valentini-noisy)]([https://www.kaggle.com/datasets/muhmagdy/valentini-noisy](https://www.kaggle.com/datasets/muhmagdy/valentini-noisy))  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Loss** \\ **Function** \\ **(a,f)** \\ \end{tabular} } & \multicolumn{6}{c}{**Classical U-Net with CCABs**} & \multicolumn{6}{c}{**Advanced U-Net with GLFBs**} \\ \cline{2-13}  & \multirow{2}{*}{**SNR**} & \multirow{2}{*}{**Resulting**} & \multirow{2}{*}{**CSIG**} & \multirow{2}{*}{**CBAK**} & \multirow{2}{*}{**COVL**} & \multirow{2}{*}{**PESQ**} & \multicolumn{2}{c}{**STOI**} & \multirow{2}{*}{**Resulting**} & \multirow{2}{*}{**SNR (dB)**} & \multirow{2}{*}{**CSIG**} & \multirow{2}{*}{**CBAK**} & \multirow{2}{*}{**COVL**} & \multirow{2}{*}{**PESQ**} & \multirow{2}{*}{**STOI**} \\  & & & & & & & & & & & & & **(\%)** \\ \hline \multirow{8}{*}{(0.5, 1)} & \multirow{8}{*}{7.5 dB} & \multirow{8}{*}{18.26} & \multirow{8}{*}{4.412} & 3.627 & 3.870 & 3.326 & 2.837 & 81.28 & 13.19 & 3.700 & 3.103 & 3.245 & 2.  
(accessed on 24 September 2024)] and DEMAND database [[[https://www.kaggle.com/datasets/chrisflo/demand?resource=download](https://www.kaggle.com/datasets/chrisflo/demand?resource=download)]([https://www.kaggle.com/datasets/chrisflo/demand?resource=download](https://www.kaggle.com/datasets/chrisflo/demand?resource=download)) (accessed on 24 September 2024)], respectively. The programs implemented in MATLAB(r) code are available upon reasonable request.  
The authors declare no conflicts of interest.