Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes

@article{Kumar2018KnowledgeTF,
title={Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes},
author={Anurag Kumar and Maksim Khadkevich and Christian F{\"u}gen},
journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2018},
pages={326-330}
}
• Published 4 November 2017
• Computer Science
• 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study…
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