A neural network approach for speech enhancement and noise-robust bandwidth extension

When processing noisy utterances with varying frequency bandwidths using an enhancement model, the effective bandwidth of the resulting enhanced speech often remains unchanged. However, high-frequency components are crucial for perceived audio quality, underscoring the need for noise-robust bandwidth extension capabilities in speech enhancement networks. In this study, we addressed this challenge by proposing a novel network architecture and loss function based on the CAUNet, which is a state-of-the-art speech enhancement method. We introduced a multi-scale loss and implemented a coordinate embedded upsampling block to facilitate bandwidth extension while maintaining the ability of speech enhancement. Additionally, we proposed a gradient loss function to promote the neural network's convergence, leading to significant performance improvements. Our experimental results validate these modifications and clearly demonstrate the superiority of our approach over competing methods.