Most audio tagging models are trained with one-hot labels as supervised information. However, one-hot labels treat all sound events equally, ignoring the semantic hierarchy and proximity relationships between sound events. In contrast, the event descriptions contains richer information, describing the distance between different sound events with semantic proximity. In this paper, we explore the impact of training audio tagging models with auxiliary text descriptions of sound events. By aligning the audio features with the text features of corresponding labels, we inject the hierarchy and proximity information of sound events into audio encoders, improving the performance while making the prediction more consistent with human perception. We refer to this approach as Semantic Proximity Alignment (SPA). We use Ontology-aware mean Average Precision (OmAP) as the main evaluation metric for the models. OmAP reweights the false positives based on Audioset ontology distance and is more consistent with human perception compared to mAP. Experimental results show that the audio tagging models trained with SPA achieve higher OmAP compared to models trained with one-hot labels solely (+1.8 OmAP). Human evaluations also demonstrate that the predictions of SPA models are more consistent with human perception.

翻译：大多数音频标记模型使用独热标签作为监督信息进行训练。然而，独热标签将所有声音事件等同对待，忽略了声音事件之间的语义层次和邻近关系。相比之下，事件描述包含更丰富的信息，通过语义邻近性描述不同声音事件之间的距离。本文探讨了使用声音事件的辅助文本描述训练音频标记模型的影响。通过将音频特征与对应标签的文本特征进行对齐，我们将声音事件的层次结构和邻近信息注入音频编码器，从而在提升性能的同时使预测结果更符合人类感知。我们将此方法称为“语义邻近对齐”（Semantic Proximity Alignment，SPA）。使用基于本体的平均精度（Ontology-aware mean Average Precision，OmAP）作为模型的主要评估指标。OmAP根据Audioset本体距离对假阳性进行重新加权，与mAP相比更符合人类感知。实验结果表明，与仅使用独热标签训练的模型相比，采用SPA训练的音频标记模型获得了更高的OmAP（提升1.8个OmAP）。人工评估也表明，SPA模型的预测结果更符合人类感知。