# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Performance Evaluation  
In case of the matched noise conditions, we measured the objective metrics and averaged them over each SNR environment to evaluate performance for ten different noise types. Table 1 presents the PESQ scores, segSNR, eSTOI, and SDR values obtained in the matched noise conditions where the column \"_noisy_\" refers to the results obtained from the clean and the unprocessed noisy speech. The cases with SNR equal to \(-6\) and \(9\) dB indicate the unseen SNR conditions that were not included during the training phase. Firstly, we investigated whether the use of noise information improves performance for speech enhancement. The results show that the \(snT\) model, which constructed the masks using both speech and noise information, performed better than the \(sT\) model whose prediction was based only on speech components. Similarly, the \(snDT\) model with noise estimates reported better performance in terms of all the metrics compared to the \(nDT\) model. The \(nDT\) model, which disentangles the noise components in the latent feature space, resulted in lower performance improvements in comparison with the \(snT\) model. This confirms that even though the \(nDT\) model incorporated disentangled feature learning, it was not able to exploit the noise information to construct the masks during the speech enhancement process. On the other hand, in order to examine the sole effect of the disentangled feature learning, the \(nDT\) model should  
Figure 3: The architectures of the baseline models. be compared to the \(sT\) model whose structure is identical to the \(nDT\) model except for the noise disentangler. As can be seen in the results, the \(nDT\) model outperformed the \(sT\) model in terms of all the metrics. Furthermore, the comparison of the \(snDT\) model to the \(snT\) model, both of which similarly adopted the masks except that the \(snDT\) model additionally applied disentangled feature learning, reported better performance improvements for the \(snDT\) model. In summary, the proposed model showed better performance than all the other baseline models in terms of speech quality, intelligibility, noise reduction, and speech distortion, indicating that the disentanglement between speech and noise features in the latent feature space was more effective for the prediction of the clean speech. In case of the mismatched noise conditions, we evaluated performance given four different noise types and averaged the results over each of the SNR environment. Table 2 presents the PESQ scores, segSNR, eSTOI, and SDR values obtained under the mismatched noise conditions. The results show that the \(snDT\) model outperformed the baseline methods, implying that it was more robust to the unseen noise types. Since the \(snDT\) model learned how to disentangle speech components from the latent features, the disentangled features could be obtained even in the mismatched noise conditions. From the perspective of noise reduction, in particular, it is quite noteworthy that the models using disentangled feature learning showed relatively competitive performance improvements in the mismatched noise conditions compared to the matched conditions. In case of the matched noise conditions, the relative improvement of segSNR was 16.31% for the \(nDT\) model when compared against the \(sT\) model, and 11.21% for the \(snDT\) model against the \(snT\) model. In the case of the mismatched noise conditions, however, the relative improvements of segSNR of the \(nDT\) and \(snDT\) models were 38.79% and 15.95%, respectively. It can be seen that the proposed approach is particularly effective in the aspect of noise reduction.