MSARN: A Multi-scale Attention Residual Network for End-to-End Environmental Sound Classification-Reference-Cited by-同舟云学术

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MSARN: A Multi-scale Attention Residual Network for End-to-End Environmental Sound Classification

Published:2023-08-14 Issue:8 Volume:55 Page:11449-11465
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Hu Fucai,Song Peng,He Ruhan,Yan Zhaoli,Yu Yongsheng

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-023-11383-1.pdf

Reference36 articles.

1. Luo L, Zhang L, Wang M, Liu Z, Liu X, He R, Jin Y (2021) A system for the detection of polyphonic sound on a university campus based on CapsNet-RNN. IEEE Access 9:147900–147913

2. Rao A, Huynh E, Royston T, Kornblith A, Roy S (2018) Acoustic methods for pulmonary diagnosis. IEEE Rev Biomed Eng

3. Tanaka K, Ichikawa K, Kittiwatanawong K, Arai N, Mitamura H (2021) Automated classification of dugong calls and tonal noise by combining contour and MFCC features. Acoust Aust 49:385–394

4. Piczak K (2015) Environmental sound classification with convolutional neural networks. In: 2015 IEEE 25th international workshop on machine learning for signal processing (MLSP), pp 1–6

5. Agrawal D, Sailor H, Soni M, Patil H (2017) Novel TEO-based Gammatone features for environmental sound classification. In: 2017 25th European signal processing conference (EUSIPCO), pp 1809–1813

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