Progressive Subband Modeling for Artifacts-free Speech Super-resolution

Abstract

In this paper, we consider new reconstruction loss together with a subband objective in the form of auxiliary loss function for artifacts-free speech super-resolution. Unlike prior work which mainly consider full band of frequency region for speech super-resolution, the proposed method alleviates distortion generated during deep learning training via subband modeling. To further minimize spectral artifacts, we also apply progressive curriculum learning for superior performance. Our experimental results demonstrate that the proposed method outperforms the evaluated baselines on the both TIMIT and VCTK dataset by increase in both intelligibility and perceptual score. Furthermore, the visual representation of spectrograms comparison verify that our proposed method clearly restoring speech with fewer artifacts. Audio samples and the implementations are available online.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>