Ontological Learning from Weak Labels

TL;DR

This paper explores how ontological information can enhance learning from weakly labeled audio data, using Graph Convolutional Networks to model concept relationships, and evaluates their effectiveness on the AudioSet dataset.

Contribution

It introduces a GCN-based approach to incorporate ontology knowledge into weakly supervised audio event classification, improving upon baseline models.

Findings

GCN captures ontology knowledge better for weak, multi-labeled data

Best GCN model achieves higher mAP and AUC scores for concepts

Ontology information does not significantly outperform models without it

Abstract

Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts. In this work, we seek to investigate whether using this ontological information will improve learning from weakly labeled data, which are easier to collect since it requires only the presence or absence of an event to be known. We use the AudioSet ontology and dataset, which contains audio clips weakly labeled with the ontology concepts and the ontology providing the "Is A" relations between the concepts. We first re-implemented the model proposed by soundevent_ontology with modification to fit the multi-label scenario and then expand on that idea by using a Graph Convolutional Network (GCN) to model the ontology information to learn the concepts. We find that the baseline Siamese does not perform better by incorporating ontology information in the weak and multi-label scenario, but that the GCN does capture the ontology knowledge better for weak, multi-labeled data. In our experiments, we also investigate how different modules can tolerate noises introduced from weak labels and better incorporate ontology information. Our best Siamese-GCN model achieves mAP=0.45 and AUC=0.87 for lower-level concepts and mAP=0.72 and AUC=0.86 for higher-level concepts, which is an improvement over the baseline Siamese but about the same as our models that do not use ontology information.