Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
This work addresses speech enhancement in noisy environments, offering an incremental improvement by refining phase handling in existing models.
The paper tackles the problem of phase reconstruction in neural speech enhancement by introducing a phase continuity loss that considers relative phase variations across time and frequency, resulting in improved quality of enhanced speech signals over the baseline when trained jointly with magnitude spectrum loss.
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms are typically designed to reduce the distortion of phase spectrum values at specific frequencies, which ensures they do not significantly affect the quality of the enhanced speech. In this paper, we propose an effective phase reconstruction strategy for neural speech enhancement that can operate in noisy environments. Specifically, we introduce a phase continuity loss that considers relative phase variations across the time and frequency axes. By including this phase continuity loss in a state-of-the-art neural speech enhancement system trained with reconstruction loss and a number of magnitude spectral losses, we show that our proposed method further improves the quality of enhanced speech signals over the baseline, especially when training is done jointly with a magnitude spectrum loss.