An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification
With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks. This paper introduces an end-to-end method for environmental sound classification based on a one-dimensional convolution neural network w...
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my.utp.eprints.237832021-08-19T13:10:02Z An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification Ragab, M.G. Abdulkadir, S.J. Aziz, N. Alhussian, H. Bala, A. Alqushaibi, A. With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks. This paper introduces an end-to-end method for environmental sound classification based on a one-dimensional convolution neural network with Bayesian optimization and ensemble learning, which directly learns features representation from the audio signal. Several convolutional layers were used to capture the signal and learn various filters relevant to the classification problem. Our proposed method can deal with any audio signal length, as a sliding window divides the signal into overlapped frames. Bayesian optimization accomplished hyperparameter selection and model evaluation with cross-validation. Multiple models with different settings have been developed based on Bayesian optimization to ensure network convergence in both convex and non-convex optimization. An UrbanSound8K dataset was evaluated for the performance of the proposed end-to-end model. The experimental results achieved a classification accuracy of 94.46, which is 5 higher than existing end-to-end approaches with fewer trainable parameters. Four measurement indices, namely: sensitivity, specificity, accuracy, precision, recall, F-measure, area under ROC curve, and the area under the precision-recall curve were used to measure the model performance. The proposed approach outperformed state-of-the-art end-to-end approaches that use hand-crafted features as input in selected measurement indices and time complexity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107178909&doi=10.3390%2fapp11104660&partnerID=40&md5=91f99b7311200d1e63fd943364e2c341 Ragab, M.G. and Abdulkadir, S.J. and Aziz, N. and Alhussian, H. and Bala, A. and Alqushaibi, A. (2021) An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification. Applied Sciences (Switzerland), 11 (10). http://eprints.utp.edu.my/23783/ |
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With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks. This paper introduces an end-to-end method for environmental sound classification based on a one-dimensional convolution neural network with Bayesian optimization and ensemble learning, which directly learns features representation from the audio signal. Several convolutional layers were used to capture the signal and learn various filters relevant to the classification problem. Our proposed method can deal with any audio signal length, as a sliding window divides the signal into overlapped frames. Bayesian optimization accomplished hyperparameter selection and model evaluation with cross-validation. Multiple models with different settings have been developed based on Bayesian optimization to ensure network convergence in both convex and non-convex optimization. An UrbanSound8K dataset was evaluated for the performance of the proposed end-to-end model. The experimental results achieved a classification accuracy of 94.46, which is 5 higher than existing end-to-end approaches with fewer trainable parameters. Four measurement indices, namely: sensitivity, specificity, accuracy, precision, recall, F-measure, area under ROC curve, and the area under the precision-recall curve were used to measure the model performance. The proposed approach outperformed state-of-the-art end-to-end approaches that use hand-crafted features as input in selected measurement indices and time complexity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
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Ragab, M.G. Abdulkadir, S.J. Aziz, N. Alhussian, H. Bala, A. Alqushaibi, A. |
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Ragab, M.G. Abdulkadir, S.J. Aziz, N. Alhussian, H. Bala, A. Alqushaibi, A. An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
author_facet |
Ragab, M.G. Abdulkadir, S.J. Aziz, N. Alhussian, H. Bala, A. Alqushaibi, A. |
author_sort |
Ragab, M.G. |
title |
An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
title_short |
An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
title_full |
An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
title_fullStr |
An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
title_full_unstemmed |
An ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
title_sort |
ensemble one dimensional convolutional neural network with bayesian optimization for environmental sound classification |
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MDPI AG |
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2021 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107178909&doi=10.3390%2fapp11104660&partnerID=40&md5=91f99b7311200d1e63fd943364e2c341 http://eprints.utp.edu.my/23783/ |
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13.18916 |