## 4 Results and Evaluation
### Objective Evaluations
#### 4.1.2 Evaluation of Background Noise Suppression Based on Noise Type  
This evaluation quantifies the differences between the spectrogram of the original audio, sourced from the TED-LIUM 3 dataset before adding background noise, and two other spectrograms: one corresponding to the audio with background noise and the other to the denoised audio. The relationship between these values serves as a measure of the level of noise suppression relative to the original clean signal. In other words, it helps us identify which type of background noise has been most effectively suppressed. By referencing both values to the same baseline--the original TED talk audio that is free of noise--we obtain a common metric for all scenarios. The difference between these metrics indicates the effectiveness of noise reduction in each case. Specifically, the better the second measurement (the difference between the restored audio spectrogram and the original audio spectrogram) is compared to the first (the difference between the noisy audio spectrogram and the original audio spectrogram), the more effectively the background noise has been eliminated in that particular use case.  
We will use two metrics applied to the spectrograms: again, the MSE and the Structural Similarity Index Measure (SSIM). The SSIM was introduced by [38] and measures the perceptual similarity between images regardless of which is of better quality. It considers three image features, luminance (I), contrast (c), and structure (s), that are weighted through three constants: \(\alpha\), \(\beta\), and \(\gamma\). SSIM is calculated using Equation (11):  
\[SSIM(x,y)=[l(x,y)]^{\alpha}\cdot[c(x,y)]^{\beta}\cdot[s(x,y)]^{\gamma} \tag{11}\]  
This evaluation has used 100 samples for each scenario from the previous evaluation. So, 400 instances have been evaluated in total. Regarding the scikit-image library, \(\alpha\), \(\beta\), and \(\gamma\) values by default are 0.01, 0.03, and 1.5, respectively [39].  
For both metrics, the same analysis can be applied. The suppression of wind noise yields the poorest performance. Both in absolute terms and as a percentage, the difference in the MSE and SSIM values is the smallest. In the case of the MSE, which measures the pixel-to-pixel similarity between spectrograms, the restored audio does resemble the original more than the noisy audio does, but by a smaller percentage (37.2%) compared to other scenarios. It is worth noting that these are the cases that most closely resemble the original audio, meaning that the distortion introduced by the noise is the lowest among all of the use cases. Therefore, the margin for improvement is smaller, as will be reflected in the subjective assessment where noise cleanliness depends on auditory perception. In the other scenarios, although the MSE of the restored audio is still high, the reduction compared to the audio with background noise is much greater. Specifically, in the case of thunderstorms and rainfall, the quality of the work is significantly higher. This is also noteworthy considering that these types of background noise most severely affect the intelligibility of the original spoken segments. A similar analysis and conclusion apply when considering the SSIM metric instead of the MSE. In this case, storms are the best, wind is the worst, and traffic interchanges its position with aircrafts. The results of Table 4 confirm what the comparison with the baselines describes in Table 3.