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In this paper, we introduce a novel attentional similarity module for theproblem of few-shot sound recognition. Given a few examples of an unseen soundevent, a classifier must be quickly adapted to recognize the new sound eventwithout much fine-tuning. The proposed attentional similarity module can beplugged into any metric-based learning method for few-shot learning, allowingthe resulting model to especially match related short sound events. Extensiveexperiments on two datasets shows that the proposed module consistentlyimproves the performance of five different metric-based learning methods forfew-shot sound recognition. The relative improvement ranges from +4.1% to +7.7%for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% fornoiseESC-50. Qualitative results demonstrate that our method contributes inparticular to the recognition of transient sound events.

Learning to match transient sound events using attentional similarity for few-shot sound recognition