Deep Convolutional Neural Network with Mixup for Environmental Sound Classification

2018/08/25 by Zhichao Zhang, Zhang, Zhichao, Shugong Xu +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Animal Vocal Communication and Behavior #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1808.08405

openalex publication_date 2018/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
• mixup: Beyond Empirical Risk Minimization
• Learning from Between-class Examples for Deep Sound Recognition

Related

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• Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features
• CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification
• SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
• End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network