  This paper studies the Speech Enhancement based on Deep Neural Networks. The
proposed architecture gradually follows the signal transformation during
enhancement by means of a visualization probe at each network block. Alongside
the process, the enhancement performance is visually inspected and evaluated in
terms of regression cost. This progressive scheme is based on Residual
Networks. During the process, we investigate a residual connection with a
constant number of channels, including internal state between blocks, and
adding progressive supervision. The insights provided by the interpretation of
the network enhancement process leads us to design an improved architecture for
the enhancement purpose. Following this strategy, we are able to obtain speech
enhancement results beyond the state-of-the-art, achieving a favorable
trade-off between dereverberation and the amount of spectral distortion.
