Despite the remarkable progress of deep learning on speech recognition and music processing, it is still challenging to classify general audio signals due to the high cost of collection and annotation of the samples. The ability to learn discriminative features from a small dataset makes deep metric learning a promising method for general audio classification. However, because of the difficulty in mining informative sample pairs, it usually suffers from slow convergence or even poor local minima. In this letter, to improve classification performance by exploiting the advantages of both the weight-based loss and the metric-based loss, we proposed a multi-positive metric loss and a framework to joint it with the common softmax loss. The proposed method eliminates the need for sub-loss weighting by measuring the similarity between samples in a consistent probabilistic form. It also enhances the classification performance by improving the estimation of the intra-class and inter-class relationships from multiple positive samples. Finally, we evaluated the proposed method on the ShipsEar dataset and the Ocean Networks Canada dataset, and the results verified its effectiveness.

中文翻译：

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船舶辐射噪声分类的深度神经网络判别集合损失

尽管在语音识别和音乐处理方面的深度学习取得了显着进展，但是由于样本的收集和注释成本较高，因此难以对一般音频信号进行分类。从小数据集中学习判别特征的能力使深度度量学习成为一般音频分类的一种有前途的方法。但是，由于难以挖掘信息丰富的样本对，因此通常会出现收敛缓慢甚至局部极小值的问题。在这封信中，为了通过利用基于权重的损失和基于度量的损失两者的优点来提高分类性能，我们提出了一个多正度量损失以及将其与常见的softmax损失结合在一起的框架。所提出的方法通过以一致的概率形式测量样本之间的相似性，消除了对子损失加权的需要。通过改进对多个正样本的类内和类间关系的估计，它还提高了分类性能。最后，我们在ShipsEar数据集和加拿大海洋网络数据集上评估了该方法，结果证明了该方法的有效性。