Joint analysis of acoustic scenes and sound events based on multitask learning with dynamic weight adaptation

Author:

Nada Kayo1,Imoto Keisuke1,Tsuchiya Takao1

Affiliation:

1. Faculty of Science and Engineering, Doshisha University

Publisher

Acoustical Society of Japan

Subject

Acoustics and Ultrasonics

Reference35 articles.

1. 1) D. Chakrabarty and M. Elhilali, "Abnormal sound event detection using temporal trajectories mixtures," Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) 2016, pp. 216–220 (2016).

2. 2) Y. Koizumi, Y. Kawaguchi, K. Imoto, T. Nakamura, Y. Nikaido, R. Tanabe, H. Purohit, K. Suefusa, T. Endo, M. Yasuda and N. Harada, "Description and discussion on DCASE2020 challenge task2: Unsupervised anomalous sound detection for machine condition monitoring," Proc. Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Workshop, pp. 81–85 (2020).

3. 3) C. Chan and E. W. M. Yu, "An abnormal sound detection and classification system for surveillance applications," Proc. Eur. Signal Process. Conf. (EUSIPCO) 2010, pp. 1851–1855 (2010).

4. 4) J. A. Stork, L. Spinello, J. Silva and K. O. Arras, "Audio-based human activity recognition using non-Markovian ensemble voting," Proc. IEEE Int. Symp. Robot and Human Interactive Communication (RO-MAN) 2012, pp. 509–514 (2012).

5. 5) K. Imoto, S. Shimauchi, H. Uematsu and H. Ohmuro, "User activity estimation method based on probabilistic generative model of acoustic event sequence with user activity and its subordinate categories," Proc. Interspeech 2013 (2013).

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