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Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
- Source :
- ICASSP
- Publication Year :
- 2018
- Publisher :
- arXiv, 2018.
-
Abstract
- 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 loss. We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.<br />Comment: Accepted for the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Sound (cs.SD)
Computer science
business.industry
Pattern recognition
Machine Learning (stat.ML)
Computer Science - Sound
Machine Learning (cs.LG)
Recurrent neural network
Statistics - Machine Learning
Audio and Speech Processing (eess.AS)
FOS: Electrical engineering, electronic engineering, information engineering
Artificial intelligence
business
Electrical Engineering and Systems Science - Audio and Speech Processing
Subjects
Details
- ISSN :
- 2379190X
- Database :
- OpenAIRE
- Journal :
- ICASSP
- Accession number :
- edsair.doi.dedup.....020e50c4ea574c8069f732939b609d11
- Full Text :
- https://doi.org/10.48550/arxiv.1811.01092