# Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

arXiv: https://arxiv.org/abs/1805.07319 · HTML: https://mlsanity.com/paper/1805.07319/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 16 of 16)

- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices](https://mlsanity.com/paper/2203.12314/) (2022)
- [Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics](https://mlsanity.com/paper/2404.16436/) (2024)
- [A Light-weight Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2302.13028/) (2023)
- [A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2211.02820/) (2022)
- [Interpretability Analysis of Deep Models for COVID-19 Detection](https://mlsanity.com/paper/2211.14372/) (2022)
- [Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data](https://mlsanity.com/paper/2309.09970/) (2023)
- [Low-complexity deep learning frameworks for acoustic scene classification](https://mlsanity.com/paper/2206.06057/) (2022)
- [Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network](https://mlsanity.com/paper/2206.13392/) (2022)
- [Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context](https://mlsanity.com/paper/2210.08610/) (2022)
- [Acoustic scene classification using auditory datasets](https://mlsanity.com/paper/2112.13450/) (2021)
- [Maximizing Audio Event Detection Model Performance on Small Datasets Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation Study](https://mlsanity.com/paper/2202.03514/) (2022)
- [The AI Mechanic: Acoustic Vehicle Characterization Neural Networks](https://mlsanity.com/paper/2205.09667/) (2022)
- [Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms](https://mlsanity.com/paper/2305.09463/) (2023)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)
- [Environmental Sound Deepfake Detection Using Deep-Learning Framework](https://mlsanity.com/paper/2604.19652/) (2026)

## Abstract (arXiv)

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data argumentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.