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Title: 

Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) for Sound Event Detection

Authors: Chan, Teck Kai
Chin, Cheng Siong
Li, Ye
Date Issued: Oct-2019
Citation: T. Chan, C. Chin & Y. Li, "Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) for Sound Event Detection", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 40–44, New York University, NY, USA, Oct. 2019
Abstract: The main scientific question of this year DCASE chal-lenge, Task 4 - Sound Event Detection in Domestic Environments, is to investigate the types of data (strongly labeled synthetic data, weakly labeled data, unlabeled in domain data) required to achieve the best performing system. In this paper, we proposed a deep learning model that integrates Non-Negative Matrix Factorization (NMF) with Convolutional Neural Net-work (CNN). The key idea of such integration is to use NMF to provide an approximate strong label to the weakly labeled data. Such integration was able to achieve a higher event based F1-score as compared to the baseline system (Evaluation Dataset: 30.39% vs. 23.7%, Validation Dataset: 31% vs. 25.8%). By com-paring the validation results with other participants, the proposed system was ranked 8th among 19 teams (inclusive of the baseline system) in this year Task 4 challenge.
First Page: 40
Last Page: 44
DOI: https://doi.org/10.33682/50ef-dx29
Type: Article
Appears in Collections:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

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