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Detecting Sound Events Using Convolutional Macaron Net With Pseudo Strong Labels

Lookup NU author(s): Teck CHAN, Professor Cheng Chin


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In this paper, we propose to address the issue of the lack of strongly labeled data by using pseudo strongly labeled data that is approximated using Convolutive Nonnegative Matrix Factorization (CNMF). Using this pseudo strongly labeled data, we then train a new architecture combining Convolutional Neural Network (CNN) with Macaron Net (MN), which we term it as Convolutional Macaron Net (CMN). As opposed to the Mean-Teacher approach which trains two similar models synchronously, we propose to train two different CMNs synchronously where one of the models will provide the frame-level prediction while the other will provide the clip level prediction. Based on our proposed framework, our system outperforms the baseline system of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4 by a margin of over 10%. By comparing with the first place of the challenge which utilize a combination of CNN and Conformer, our system also marginally wins it by 0.3%.

Publication metadata

Author(s): Chan TK, Chin CS

Publication type: Article

Publication status: Published

Journal: arXiv

Year: 2020

Volume: 2009.09632

Pages: 1-5

Print publication date: 21/09/2020

Online publication date: 21/09/2020

Acceptance date: 21/09/2020

Publisher: Cornell University