Deep Convolutional Neural Network with Mixup for Environmental Sound Classification
Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noiselike nature and may be produced by a wide variety of sources. In this paper, we propose to use a novel deep convolutional neural network for ESC tasks. Our network architecture uses stacked convolutional and pooling layers to extract high-level feature representations from spectrogram-like features. Furthermore, we apply mixup to ESC tasks and explore its impacts on classification performance and feature distribution. Experiments were conducted on UrbanSound8K, ESC-50 and ESC-10 datasets. Our experimental results demonstrated that our ESC system has achieved the state-of-the-art performance (83.7%) on UrbanSound8K and competitive performance on ESC-50 and ESC-10.
Environmental sound classification Convolutional neural network Mixup
Zhichao Zhang Shugong Xu Shan Cao Shunqing Zhang
Shanghai Institute for Advanced Communication and Data Science,Shanghai University,Shanghai 200444,China
国际会议
广州
英文
356-367
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）