# Deep Convolutional Neural Network with Mixup for Environmental Sound Classification

arXiv: https://arxiv.org/abs/1808.08405 · HTML: https://mlsanity.com/paper/1808.08405/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 15 of 15)

- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition](https://mlsanity.com/paper/2102.12664/) (2021)
- [Survey: Image Mixing and Deleting for Data Augmentation](https://mlsanity.com/paper/2106.07085/) (2021)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification](https://mlsanity.com/paper/2103.01929/) (2021)
- [Automatic Detection Of Noise Events at Shooting Range Using Machine Learning](https://mlsanity.com/paper/2107.11453/) (2021)
- [GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage](https://mlsanity.com/paper/2106.10277/) (2021)
- [Generative AI-based data augmentation for improved bioacoustic classification in noisy environments](https://mlsanity.com/paper/2412.01530/) (2024)
- [Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification](https://mlsanity.com/paper/2107.13180/) (2021)
- [An empirical investigation into audio pipeline approaches for classifying bird species](https://mlsanity.com/paper/2108.04449/) (2021)
- [Improving Spoken Language Identification with Map-Mix](https://mlsanity.com/paper/2302.08229/) (2023)
- [Studying the Effect of Audio Filters in Pre-Trained Models for Environmental Sound Classification](https://mlsanity.com/paper/2408.13644/) (2024)
- [ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning](https://mlsanity.com/paper/2409.14043/) (2024)
- [FICAug: Feature-Informed Clustering and Augmentation for Facial-Expression-Based Parkinson's Disease Screening](https://mlsanity.com/paper/2409.17685/) (2024)
- ["What's That Sound?": A Versatile, Robust, and Lightweight Convolutional Transformer for Environment Sound Recognition](https://mlsanity.com/paper/2609.27762/) (2026)

## Abstract (arXiv)

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.