  Residual and skip connections play an important role in many current
  generative models. Although their theoretical and numerical advantages
  are understood, their role in speech enhancement systems has not been
  investigated so far. When performing spectral speech enhancement,
  residual connections are very similar in nature to spectral subtraction,
  which is the one of the most commonly employed speech enhancement approaches.
   Highway networks, on the other hand, can be seen as a combination of spectral
  masking and spectral subtraction. However, when using deep neural networks, such operations would
  normally happen in a transformed spectral domain, as opposed to traditional speech
  enhancement where all operations are often done directly on the spectrum.
   In this paper, we aim to investigate the role of residual and highway
  connections in deep neural networks for speech enhancement, and verify whether
  or not they operate similarly to their traditional, digital signal processing
  counterparts. We visualize the outputs of such connections, projected back to
  the spectral domain, in models trained for speech denoising, and show that while
  skip connections do not necessarily improve performance with regards to the
  number of parameters, they make speech enhancement models more interpretable.
We show how using skip connections can make speech enhancement models more interpretable, as it makes them use similar mechanisms that have been explored in the DSP literature. The authors propose incorporating Residual, Highway and Masking blocks inside a fully convolutional pipeline in order to understand how iterative inference of the output and the masking is performed in a speech enhancement task The authors interpret highway, residual and masking connections.  The authors generate their own noisy speech by artificially adding noise from a well established noise data-set to a less know clean speech data-set.