Learning to Match Transient Sound Events Using Attentional Similarity for Few-shot Sound Recognition

In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module c...

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Bibliographic Details
Main Authors: Chou, Szu-Yu, Cheng, Kai-Hsiang, Jang, Jyh-Shing Roger, Yang, Yi-Hsuan
Format: Conference Proceeding
Language:English
Subjects:
deep learning
Feature extraction
Few-shot learning
Image color analysis
Learning systems
Noise measurement
sound event detection
Task analysis
Training
Transient analysis
transient sound event
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Summary:In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets show that the proposed module consistently improves the performance of five different metric-based learning methods for few-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% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682558