The Study of Improving the Adaptive FullSubNet+ Speech Enhancement Framework with Selective Wavelet Packet Decomposition Sub-Band Features

Abstract

State-of-the-art speech enhancement techniques use deep neural networks to improve distorted speech signals. These networks employ an encoder-decoder framework, with the encoder extracting features from the input signal. Our research suggests using discrete wavelet transform (DWT) features as an alternative to existing methods. DWT features work well with time-domain features and improve performance in the adaptive FullSubNet+ framework. This study proposes using wavelet packet decomposition (WPD) to extract features and discarding sub-band WPD features that harm performance. Our method outperforms the original A-FSN in objective speech metrics, making it a promising speech enhancement framework.