Research Trends in Environmental Sound Analysis and Anomalous Sound Detection

Author:

IMOTO Keisuke1,KAWAGUCHI Yohei2

Affiliation:

1. Faculty of Science and Engineering, Doshisha University

2. Hitachi, Ltd. R&D.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference124 articles.

1. (1) S. Ntalampiras, “Audio pattern recognition of baby crying sound events,” Journal of the Audio Engineering Society, vol. 63, no. 5, pp. 358-369, 2015.

2. (2) T. Zhang and C.J. Kuo, “Audio content analysis for online audiovisual data segmentation and classification,” IEEE Trans. Audio, Speech, Language Process., vol. 9, no. 4, pp. 441-457, 2001.

3. (3) Q. Jin, P.F. Schulam, S. Rawat, S. Burger, D. Ding, and F. Metze, “Event-based video retrieval using audio,” Proc. INTERSPEECH, pp. 2085-2088, 2012.

4. (4) 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. Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), pp. 81-85, 2020.

5. (5) Y.T. Peng, C.Y. Lin, M.T. Sun, and K.C. Tsai, “Healthcare audio event classification using hidden Markov models and hierarchical hidden Markov models,” Proc. IEEE International Conference on Multimedia and Expo (ICME), pp. 1218-1221, 2009.

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1. Anomalous Sound Detection System in Manufacturing Industry Using Unsupervised Learning;2024 IEEE International Conference on Consumer Electronics (ICCE);2024-01-06

2. Detection of Abnormal Sound in a Public Lavatory Using Maximum Likelihood Estimation;IEEJ Transactions on Electronics, Information and Systems;2023-09-01

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