Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network-Reference-Cited by-同舟云学术

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Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network

Published:2022-07-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
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Author:

Zhang Yu¹,Dora Shirin²,Martinez-Garcia Miguel¹,Bhattacharyaand Saugat³

Affiliation:

1. Loughborough University,Department of Aeronautical and Automotive Engineering,Loughborough,U.K.,LE11 3TU

2. Loughborough University,Department of Computer Science,Loughborough,U.K.,LE11 3TU

3. Ulster University,School of Computing, Engineering and Intelligent Systems,Londonderry,U.K.,BT48 7JL

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9863218/9863240/09863412.pdf?arnumber=9863412

Reference25 articles.

1. Fault detection of contactor using acoustic monitoring

2. Cochleagram Based Speaker Identification Using Noise Adapted CNN

3. Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

4. Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

5. HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimal Feature Selection via Bayesian Optimisation for Acoustic Condition Monitoring;2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED);2023-08-28

2. Acoustic-Based Machine Condition Monitoring—Methods and Challenges;Eng;2023-01-01

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