Deep Convolutional Neural Network with Mixup for Environmental Sound Classification
This addresses the challenging problem of classifying diverse environmental sounds for applications like audio analysis, though it appears incremental by combining existing techniques.
The paper tackled environmental sound classification by proposing a deep convolutional neural network with mixup, achieving state-of-the-art performance of 83.7% on UrbanSound8K and competitive results on other datasets.
Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noise-like 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.