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Audio and Acoustic Signal Processing
Acoustic Event Detection


Bowen Shi

Date & Time

Wed, May 6, 2020

10:00 am – 12:00 pm




We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data and facilitates personalization of AED systems for users in real applications. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few-shot classification problem. Compared to supervised baselines, meta-learning models achieve superior performance, thus showing its effectiveness on generalization to new audio events. Our analysis including impact of initialization and domain discrepancy further validate the advantage of meta-learning approaches in few-shot AED.


Bowen Shi

Toyota Technological Institute at Chicago
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Session Chair

Romain Serizel

Université de Lorraine