论文标题
通过分类器注意机制进行音频分类的深度神经网络
A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism
论文作者
论文摘要
音频分类被认为是模式识别中的一个具有挑战性的问题。最近，已经提出了许多使用深神经网络提出的算法。在本文中，我们介绍了一种新的基于注意力的神经网络体系结构，称为基于分类器注意的卷积神经网络（CAB-CNN）。该算法使用新设计的架构，该体系结构由简单分类器列表和作为分类器选择器的注意机制组成。该设计大大减少了分类器所需的参数数量及其复杂性。这样，训练分类器并实现高稳定的性能变得更加容易。实验结果证实了我们的主张。与最先进的算法相比，我们的算法在所有选定的测试分数上取得了10％以上的提高。
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.
