  For most of the state-of-the-art speech enhancement techniques, a spectrogram
is usually preferred than the respective time-domain raw data since it reveals
more compact presentation together with conspicuous temporal information over a
long time span. However, the short-time Fourier transform (STFT) that creates
the spectrogram in general distorts the original signal and thereby limits the
capability of the associated speech enhancement techniques. In this study, we
propose a novel speech enhancement method that adopts the algorithms of
discrete wavelet packet transform (DWPT) and nonnegative matrix factorization
(NMF) in order to conquer the aforementioned limitation. In brief, the DWPT is
first applied to split a time-domain speech signal into a series of subband
signals without introducing any distortion. Then we exploit NMF to highlight
the speech component for each subband. Finally, the enhanced subband signals
are joined together via the inverse DWPT to reconstruct a noise-reduced signal
in time domain. We evaluate the proposed DWPT-NMF based speech enhancement
method on the MHINT task. Experimental results show that this new method
behaves very well in prompting speech quality and intelligibility and it
outperforms the convnenitional STFT-NMF based method.
