# An Ensemble of Convolutional Neural Networks for Audio Classification

**Authors:** Loris Nanni, Gianluca Maguolo, Sheryl Brahnam, Michelangelo Paci  
**arXiv:** [2007.07966](https://arxiv.org/abs/2007.07966)  
**Date:** 2020-07-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2007.07966v2/

## Key points

- Authors present an extensive study on ensembles of classifiers for audio classification
- Study focuses on training Convolutional Neural Networks (CNNs) using various data augmentation techniques and four signal representations
- Performance of ensembles evaluated on bird calls, cat sounds, and Environmental Sound Classification datasets
- Ensembles combining different data augmentation techniques with various signal representations outperform best methods reported in literature for these datasets
- Proposed approach achieves state-of-the-art results in ESC-50 dataset
- Fusing multiple classifiers using different techniques is superior to using stand-alone classifiers
- Study represents most extensive investigation into ensembles of CNNs for audio classification to date
- Approach achieves remarkable performance improvements over existing methods by leveraging combination of data augmentation techniques and diverse signal representations
- Research contributes valuable insights into advancing audio classification techniques and provides foundation for future studies in this field

## Summary

In the paper titled "An Ensemble of Convolutional Neural Networks for Audio Classification," authors Loris Nanni, Gianluca Maguolo, Sheryl Brahnam, and Michelangelo Paci present an extensive study on ensembles of classifiers for audio classification. The study focuses on training Convolutional Neural Networks (CNNs) using various data augmentation techniques and four signal representations. The performance of these ensembles is evaluated on three freely available audio classification datasets: bird calls, cat sounds, and the Environmental Sound Classification dataset. The results of the study demonstrate that the ensembles combining different data augmentation techniques with various signal representations outperform the best methods reported in the literature for these datasets. In particular, the proposed approach achieves state-of-the-art results in the widely used ESC-50 dataset. This finding highlights not only the effectiveness of CNNs for audio classification but also the superiority of fusing multiple classifiers using different techniques compared to using stand-alone classifiers. The authors emphasize that their study represents the most extensive investigation into ensembles of CNNs for audio classification to date. By leveraging a combination of data augmentation techniques and diverse signal representations, their approach achieves remarkable performance improvements over existing methods. This research contributes valuable insights into advancing audio classification techniques and provides a solid foundation for future studies in this field. Overall, this paper presents a comprehensive exploration of ensemble-based CNN models for audio classification. The findings highlight the potential of these models to achieve state-of-the-art results and underscore the importance of utilizing diverse data augmentation techniques and signal representations in improving classification accuracy.