Generalization in supervised learning of single-channel speech enhancement
In the field of supervised learning for single-channel speech enhancement, generalization has always been a major challenge. It is crucial for models to perform well not only on the training data but also on unseen data. In this article, we will discuss a new approach called Learnable Loss Mixup (LLM) that addresses this issue and improves the generalization of deep learning-based speech enhancement models.
Loss mixup is a technique that involves optimizing a mixture of loss functions of random sample pairs to train a model on virtual training data constructed from these pairs. It has been shown to be effective in improving generalization performance in various domains. Learnable loss mixup is a special variant of loss mixup, where the loss functions are mixed using a non-linear mixing function that is automatically learned via neural parameterization and conditioned on the mixed data.
The authors of this work conducted experiments on the VCTK benchmark, which is widely used for evaluating speech enhancement algorithms. The results showed that learnable loss mixup achieved a PESQ score of 3.26, outperforming the state-of-the-art models.
This is a significant improvement in performance and demonstrates the effectiveness of the learnable loss mixup approach. By incorporating the mixed data and using a non-linear mixing function learned through neural parameterization, the model is able to better capture the complexities and variations present in real-world speech data. This enables it to generalize well on unseen data and perform better than existing models.
The success of learnable loss mixup opens up possibilities for further research and development in the field of supervised learning for single-channel speech enhancement. Future work could explore different methods for non-linear mixing function parameterization and investigate its impact on generalization performance. Additionally, it would be interesting to evaluate the performance of learnable loss mixup on other benchmark datasets and compare it against other state-of-the-art models in the field.
In conclusion, learnable loss mixup is a promising technique for improving the generalization of deep learning-based speech enhancement models. Its ability to automatically learn a non-linear mixing function through neural parameterization allows it to capture the nuances of real-world speech data and outperform existing approaches. This work contributes to advancing the field of supervised learning for single-channel speech enhancement and paves the way for future research in this area.