An Ensembled Convolutional Recurrent Neural Network approach for Automated Classroom Sound Classification-Reference-Cited by-同舟云学术

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An Ensembled Convolutional Recurrent Neural Network approach for Automated Classroom Sound Classification

Published:2024-06-25 Issue: Volume: Page:183-188
ISSN:
Container-title:2024 IEEE Conference on Artificial Intelligence (CAI)
language:
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Author:

Iqbal Rashed¹,Ritz Christian¹,Yang Jack²,Howard Sarah³,Copiaco Abigail⁴

Affiliation:

1. University of Wollongong,School of Electrical, Computer and Telecommunications Engineering,NSW,Australia

2. University of Wollongong,School of Computing and Information Technology,NSW,Australia

3. University of Wollongong,School of Education,NSW,Australia

4. University of Dubai,College of Engineering and Information Technology,Dubai,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10605128/10605229/10605561.pdf?arnumber=10605561

Reference26 articles.

1. Novel principal component analysis‐based feature selection mechanism for classroom sound classification

2. Using Data Mining and Machine Learning Approaches to Observe Technology-Enhanced Learning

3. Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

4. Sound classification using evolving ensemble models and Particle Swarm Optimization

5. Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm

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