# A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism

arXiv: https://arxiv.org/abs/2006.09815 · HTML: https://mlsanity.com/paper/2006.09815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges](https://mlsanity.com/paper/2410.03458/) (2024)
- [Improving Deep Learning Sound Events Classifiers using Gram Matrix Feature-wise Correlations](https://mlsanity.com/paper/2102.11771/) (2021)
- [Convolutional Neural Networks can achieve binary bail judgement classification](https://mlsanity.com/paper/2401.14135/) (2024)
- [Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method](https://mlsanity.com/paper/2407.10828/) (2024)

## Abstract (arXiv)

Audio classification is considered as a challenging problem in pattern recognition. Recently, many algorithms have been proposed using deep neural networks. In this paper, we introduce a new attention-based neural network architecture called Classifier-Attention-Based Convolutional Neural Network (CAB-CNN). The algorithm uses a newly designed architecture consisting of a list of simple classifiers and an attention mechanism as a classifier selector. This design significantly reduces the number of parameters required by the classifiers and thus their complexities. In this way, it becomes easier to train the classifiers and achieve a high and steady performance. Our claims are corroborated by the experimental results. Compared to the state-of-the-art algorithms, our algorithm achieves more than 10% improvements on all selected test scores.