Learning from Taxonomy: Multi-Label Few-Shot Classification for Everyday Sound Recognition

Jinhua Liang, Huy Phan, Emmanouil Benetos

Abstract: Humans categorise and structure perceived acoustic signals into hierarchies of auditory objects. The semantics of these objects are thus informative in sound classification, especially in few-shot scenarios. However, existing works have only represented audio semantics as binary labels (e.g., whether a recording contains dog barking or not), and thus failed to learn a more generic semantic relationship among labels. In this work, we introduce an ontology-aware framework to train multi-label few-shot audio networks with both relative and absolute relationships in an audio taxonomy. Specifically, we propose label-dependent prototypical networks (LaD-ProtoNet) to learn coarse-to-fine acoustic patterns by exploiting direct connections between parent and children classes of sound events. We also present a label smoothing method to take into account the taxonomic knowledge by taking into account absolute distance between two labels w.r.t the taxonomy. For evaluation in a real-world setting, we curate a new dataset, namely FSD-FS, based on the FSD50K dataset and compare the proposed methods and other few-shot classifiers using this dataset. Experiments demonstrate that the proposed method outperforms non-ontology-based methods on the FSD-FS dataset.