  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.
