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Audio and Acoustic Signal Processing
AUD-P1.2
Poster
Deep Learning for Audio Classification

A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION

Wei Wei

Date & Time

Tue, May 5, 2020

12:30 pm – 2:30 pm

Location

On-Demand

Abstract

This paper presents a domain adaptation model for sound event detection. A common challenge for sound event detection is how to deal with the mismatch among different datasets. Typically, the performance of a model will decrease if it is tested on a dataset which is different from the one that the model is trained on. To address this problem, based on convolutional recurrent neural networks (CRNNs), we propose an adapted CRNN (A-CRNN) as an unsupervised adversarial domain adaptation model for sound event detection. We have collected and annotated a dataset in Singapore with two types of recording devices to complement existing datasets in the research community, especially with respect to domain adaptation. We perform experiments on recordings from different datasets and from different recordings devices. Our experimental results show that the proposed A-CRNN model can achieve a better performance on an unseen dataset in comparison with the baseline non-adapted CRNN model.


Presenter

Wei Wei

National University of Singapore
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Session Chair