# Assessment of Self-Supervised Denoising Methods for
## 4 Discussion  
Based on the results shown in Table 3, it is evident that in experiments 1 and 2, the DCUNET-cTSTM model consistently outperformed the DCUNET model across all metrics. What is particularly remarkable is its exceptional performance when handling ES mixed with white noise and tested with clean ES. In experiment 3, the DCUNET-cTSTM model shows improved SNR and PESQ-NB compared to the DCUNET model, while other metrics remained broadly similar. Experiment 4 revealed slightly varied metric results for  
Figure 9: Spectrograms of the VF test file using pre-trained models of the four experiments.  
the two models, with the PESQ-WB of the DCUNET-cTSTM model notably surpassing that of the other model.  
Moving to the results depicted in Figure 8, we start with the first experiment, which was trained with the VBD database mixed with noise number 9 of the Ur8 database (which corresponds to street music) and tested with the ES test file. The spectrogram of the original ES test file shows distinct frequency bands with high power density in yellow, indicating strong signal presence. Also, the spectrogram of the predicted ES test file using DCUNET shows improved clarity with distinct frequency bands, though some noise is still present. The power spectral density in the low-frequency regions remains similar to the original, but the higher frequencies seem slightly more uniform, indicating some noise reduction. The spectrogram of the predicted ES test file using DCUNET-cTSTM shows further improvement in clarity with more defined frequency bands. The power spectral density appears to be more similar to the original ES test file, suggesting better noise reduction compared to the DCUNET alone.  
For experiment 2, in which the training was performed using ES wave files with additional white noise, the traces of the uniform noise distribution in the higher frequencies are still visible in the DCUNET result. In contrast, the DCUNET-cTSTM result exhibits a slightly clearer spectrogram. The low-frequency regions of both spectrograms resemble those of the original test file.  
In the third experiment, which was trained using noisy VF sound files, some diffuse noise is still present in the DCUNET results. On the one hand, DCUNET appears to preserve more of the yellow regions in the low-frequency area than the second model's output. On the other hand, DCUNET-cTSTM seems to preserve more of the yellow information in the high-frequency region than the first model's outcome.  
Lastly, in the final experiment, which was trained with the ES dataset without additional noise, the DCUNET's result shows that the voiced segments are clearer and more distinct, suggesting an improved SNR compared to the original ES wave file. The DCUNET-cTSTM's outcome demonstrates that the voiced segments are even brighter, substantially improving SNR compared to the original and the DCUNET prediction.  
Referring to Figure 9, it can be seen that the original VF test file possesses a broad range of frequencies with considerable transient signals and noise distributed throughout the spectrum, in addition to high power density at both low and high frequencies. By employing DCUNET, noise reduction is effective, primarily in high-frequency regions. Consequently, the power density is concentrated below 2000 Hz, and transient noise is diminished, resulting in a clearer signal.  
A comparison of DCUNET with DCUNET-cTSTM reveals that the latter exhibits superior noise reduction compared to the former. Furthermore, DCUNET-cTSTM displays a more focused energy distribution with clearer transient signals and minimal high-frequency noise. Therefore, DCUNET-cTSTM outperforms DCUNET in enhancing signal clarity and reducing noise. It should be noted that the VF wave file is the restored version of the ES wave file using VoiceFixer. As the DCUNET-based methods target noise reduction within sound files, it is a fact that the models eliminate a considerable amount of signal information, assuming that such segments represent noises.