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.

本文介绍了一种基于一维卷积神经网络实现的环境声音分类方法，其可以对任意长度的音频信号进行处理，并采用 Gammaone 滤波器对其进行初始化，通过进行实验，结果表明该方法可以在 UrbanSound8k 数据集上取得 89% 的平均准确率，且性能比大多数现有手工提取特征或使用 2D 表示的方法都要优异。