DCASE 2018 Challenge: Solution for Task 5

December 11, 2018 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Jeremy Chew, Yingxiang Sun, Lahiru Jayasinghe, Chau Yuen arXiv ID 1812.04618 Category eess.AS: Audio & Speech Cross-listed cs.LG, cs.SD, stat.ML Citations 2 Venue arXiv.org Last Checked 3 months ago
Abstract
To address Task 5 in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning system. The proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. With extracted features such as spectrogram and mel-frequency cepstrum coefficients from different channels, the proposed system can classify different domestic activities effectively. Experimental results obtained from the provided development dataset show that good performance with F1-score of 92.19% can be achieved. Compared with the baseline system, our proposed system significantly improves the performance of F1-score by 7.69%.
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