# An Ensemble of Convolutional Neural Networks for Audio Classification

arXiv: https://arxiv.org/abs/2007.07966 · HTML: https://mlsanity.com/paper/2007.07966/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Investigating Multi-Feature Selection and Ensembling for Audio Classification](https://mlsanity.com/paper/2206.07511/) (2022)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices](https://mlsanity.com/paper/2108.06128/) (2021)
- [Deep Neural Decision Forest for Acoustic Scene Classification](https://mlsanity.com/paper/2203.03436/) (2022)
- [Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation](https://mlsanity.com/paper/2407.19265/) (2024)
- [GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage](https://mlsanity.com/paper/2106.10277/) (2021)
- [Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation](https://mlsanity.com/paper/2602.02955/) (2026)
- [Responding to Challenge Call of Machine Learning Model Development in Diagnosing Respiratory Disease Sounds](https://mlsanity.com/paper/2111.14354/) (2021)
- [Pump It Up: Predict Water Pump Status using Attentive Tabular Learning](https://mlsanity.com/paper/2304.03969/) (2023)
- [Improving Primate Sounds Classification using Binary Presorting for Deep Learning](https://mlsanity.com/paper/2306.16054/) (2023)

## Abstract (arXiv)

In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audio classification are presented and tested on three freely available audio classification datasets: i) bird calls, ii) cat sounds, and iii) the Environmental Sound Classification dataset. The best performing ensembles combining data augmentation techniques with different signal representations are compared and shown to outperform the best methods reported in the literature on these datasets. The approach proposed here obtains state-of-the-art results in the widely used ESC-50 dataset. To the best of our knowledge, this is the most extensive study investigating ensembles of CNNs for audio classification. Results demonstrate not only that CNNs can be trained for audio classification but also that their fusion using different techniques works better than the stand-alone classifiers.