  Residual learning is a recently proposed learning framework to facilitate the
training of very deep neural networks. Residual blocks or units are made of a
set of stacked layers, where the inputs are added back to their outputs with
the aim of creating identity mappings. In practice, such identity mappings are
accomplished by means of the so-called skip or residual connections. However,
multiple implementation alternatives arise with respect to where such skip
connections are applied within the set of stacked layers that make up a
residual block. While ResNet architectures for image classification using
convolutional neural networks (CNNs) have been widely discussed in the
literature, few works have adopted ResNet architectures so far for 1D audio
classification tasks. Thus, the suitability of different residual block designs
for raw audio classification is partly unknown. The purpose of this paper is to
analyze and discuss the performance of several residual block implementations
within a state-of-the-art CNN-based architecture for end-to-end audio
classification using raw audio waveforms. For comparison purposes, we analyze
as well the performance of the residual blocks under a similar 2D architecture
using a conventional time-frequency audio represen-tation as input. The results
show that the achieved accuracy is considerably dependent, not only on the
specific residual block implementation, but also on the selected input
normalization.
