  In this paper, we present an end-to-end approach for environmental sound
classification based on a 1D Convolution Neural Network (CNN) that learns a
representation directly from the audio signal. Several convolutional layers are
used to capture the signal's fine time structure and learn diverse filters that
are relevant to the classification task. The proposed approach can deal with
audio signals of any length as it splits the signal into overlapped frames
using a sliding window. Different architectures considering several input sizes
are evaluated, including the initialization of the first convolutional layer
with a Gammatone filterbank that models the human auditory filter response in
the cochlea. The performance of the proposed end-to-end approach in classifying
environmental sounds was assessed on the UrbanSound8k dataset and the
experimental results have shown that it achieves 89% of mean accuracy.
Therefore, the propose approach outperforms most of the state-of-the-art
approaches that use handcrafted features or 2D representations as input.
Furthermore, the proposed approach has a small number of parameters compared to
other architectures found in the literature, which reduces the amount of data
required for training.
