Learning environmental sounds with end-to-end convolutional neural network

Abstract

Environmental sound classification (ESC) is usually conducted based on handcrafted features such as the log-mel feature. Meanwhile, end-to-end classification systems perform feature extraction jointly with classification and have achieved success particularly in image classification. In the same manner, if environmental sounds could be directly learned from the raw waveforms, we would be able to extract a new feature effective for classification that could not have been designed by humans, and this new feature could improve the classification performance. In this paper, we propose a novel end-to-end ESC system using a convolutional neural network (CNN). The classification accuracy of our system on ESC-50 is 5.1% higher than that achieved when using logmel-CNN with the static log-mel feature. Moreover, we achieve a 6.5% improvement in classification accuracy over the state-of-the-art logmel-CNN with the static and delta log-mel feature, simply by combining our system and logmel-CNN.