This paper introduces a zero-shot sound event classification (ZS-SEC) method to identify sound events that have never occurred in training data. In our previous work, we proposed a ZS-SEC method using sound attribute vectors (SAVs), where a deep neural network model infers attribute information that describes the sound of an event class instead of inferring its class label directly. Our previous method showed that it could classify unseen events to some extent; however, the accuracy for unseen events was far inferior to that for seen events. In this paper, we propose a new ZS-SEC method that can learn discriminative global features and local features simultaneously to enhance SAV-based ZS-SEC. In the proposed method, while the global features are learned in order to discriminate the event classes in the training data, the spectro-temporal local features are learned in order to regress the attribute information using attribute prototypes. The experimental results show that our proposed method can improve the accuracy of SAV-based ZS-SEC and can visualize the region in the spectrogram related to each attribute.

翻译：本文提出一种零样本声音事件分类（ZS-SEC）方法，用于识别训练数据中从未出现过的声音事件。在前期工作中，我们提出了一种基于声音属性向量（SAV）的ZS-SEC方法，其中深度神经网络模型推断描述事件类别声音的属性信息，而非直接推断其类别标签。前期方法虽能在一定程度上对未知事件进行分类，但针对未知事件的准确率远低于已知事件。本文提出一种新型ZS-SEC方法，可同时学习判别性全局特征与局部特征，以增强基于SAV的ZS-SEC性能。该方法在通过训练数据区分事件类别学习全局特征的同时，利用属性原型回归属性信息以学习频谱-时域局部特征。实验结果表明，所提方法能提升基于SAV的ZS-SEC准确率，并可可视化频谱图中与各属性相关的区域。