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

• URL: http://arxiv.org/abs/2006.09815v1
• Date: Sun, 14 Jun 2020 21:29:44 GMT
• Title: A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism
• Authors: Haoye Lu, Haolong Zhang, Amit Nayak
• Abstract summary: We introduce a new attention-based neural network architecture called Audio-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 selector. Compared to the state-of-the-art algorithms, our algorithm achieves more than 10% improvements on all selected test scores.
• Score: 2.3204178451683264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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