作者
Huy Phan, Oliver Y Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
发表日期
2019/5/12
研讨会论文
in Proc. ICASSP, 2019, pp. 51-55
出版商
IEEE
简介
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling. Furthermore, the output layer is designed to handle arbitrary degrees of event overlap. At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries. That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step. We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence …
引用总数
201920202021202220232024532421
学术搜索中的文章
H Phan, OY Chén, P Koch, L Pham, I McLoughlin… - ICASSP 2019-2019 IEEE International Conference on …, 2019