Binary Attribute Embeddings for Zero-Shot Sound Event Classification
Binary Attribute Embeddings for Zero-Shot Sound Event Classification
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DOI：
10.1109/gcce56475.2022.10014127
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Yihan Lin;Xun-Yu Chen;R. Takashima;T. Takiguchi
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文献类型：
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作者：
Yihan Lin;Xun-Yu Chen;R. Takashima;T. Takiguchi
在本文中，我们介绍了一种用于声音事件分类的零样本学习方法。所提出的方法使用每个声音事件类的语义嵌入，并测量语义嵌入和输入音频特征嵌入之间的兼容性。对于语义嵌入，我们新定义了属性向量，它解释了声音事件类的几个属性信息，例如音高、长度、声源的材质等。在实验中，所提出的方法比使用词嵌入作为语义嵌入的传统方法表现出更高的准确性。
In this paper, we introduce a zero-shot learning method for sound event classification. The proposed method uses a semantic embedding of each sound event class and measures the compatibility between the semantic embedding and the input audio feature embedding. For semantic embedding, we newly define attribute vector that explains several attribute information of a sound event class, such as pitch, length, material of the sound source, etc. In the experiments, the proposed method showed higher accuracy than a conventional method using word embedding as the semantic embedding.