Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement

In this study, improvements are made to Inter-SubNet, a state-of-the-art speech enhancement method. Inter-SubNet is a single-channel speech enhancement framework that enhances the sub-band spectral model by integrating global spectral information, such as cross-band relationships and patterns. Despite the success of Inter-SubNet, one crucial aspect probably overlooked by Inter-SubNet is the unequal perceptual weighting of different spectral regions by the human ear, as it employs MSE as its loss function. In addition, MSE loss has a potential convergence concern for model learning due to gradient explosion. Hence, we propose further enhancing Inter-SubNet by either integrating perceptual loss with MSE loss or modifying MSE loss directly in the learning process. Among various types of perceptual loss, we adopt the temporal acoustic parameter (TAP) loss, which provides detailed estimation for low-level acoustic descriptors, thereby offering a comprehensive evaluation of speech signal distortion. In addition, we leverage Huber loss, a combination of L1 and L2 (MSE) loss, to avoid the potential convergence issue for the training of Inter-SubNet. By the evaluation conducted on the VoiceBank-DEMAND database and task, we see that Inter-SubNet with the modified loss function reveals improvements in speech enhancement performance. Specifically, replacing MSE loss with Huber loss results in increases of 0.057 and 0.38 in WB-PESQ and SI-SDR metrics, respectively. Additionally, integrating TAP loss with MSE loss yields improvements of 0.115 and 0.196 in WB-PESQ and CSIG metrics.