  This study defines a new evaluation metric for audio tagging tasks to
overcome the limitation of the conventional mean average precision (mAP)
metric, which treats different kinds of sound as independent classes without
considering their relations. Also, due to the ambiguities in sound labeling,
the labels in the training and evaluation set are not guaranteed to be accurate
and exhaustive, which poses challenges for robust evaluation with mAP. The
proposed metric, ontology-aware mean average precision (OmAP) addresses the
weaknesses of mAP by utilizing the AudioSet ontology information during the
evaluation. Specifically, we reweight the false positive events in the model
prediction based on the ontology graph distance to the target classes. The OmAP
measure also provides more insights into model performance by evaluations with
different coarse-grained levels in the ontology graph. We conduct human
evaluations and demonstrate that OmAP is more consistent with human perception
than mAP. To further verify the importance of utilizing the ontology
information, we also propose a novel loss function (OBCE) that reweights binary
cross entropy (BCE) loss based on the ontology distance. Our experiment shows
that OBCE can improve both mAP and OmAP metrics on the AudioSet tagging task.
