Bidirectional Denoising Autoencoders Based Robust Representation Learning for Underwater Acoustic Target Signal Denoising

The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference background field in underwater acoustic channels, which brings adverse effects to underwater acoustic target recognition. To improve the rec...

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Published in:	IEEE transactions on instrumentation and measurement Vol. 71; p. 1
Main Authors:	Dong, Yafen, Shen, Xiaohong, Wang, Haiyan
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Background noise bidirectional denoising autoencoder Correlation Interference Learning Noise measurement Noise reduction pseudo clean label representation learning Representations Robustness Signal denoising Support vector machines Surface waves Target recognition Training Underwater acoustic target signal denoising Underwater acoustics
ISSN:	0018-9456, 1557-9662
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Abstract	The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference background field in underwater acoustic channels, which brings adverse effects to underwater acoustic target recognition. To improve the recognition accuracy of underwater targets under background noise interference, a bidirectional denoising autoencoder (BDAE) is proposed in this paper for underwater acoustic target signal denoising robust representation learning. The proposed bidirectional denoising autoencoder is an extension of the regular denoising autoencoder, which uses the original underwater acoustic target signals and their corresponding denoised signals to learn robust representations. We then measure the usefulness of the learned representations using a support vector machine classifier. Our proposed approach is verified on the ShipsEar database. Experimental results indicate that the proposed bidirectional denoising autoencoder can effectively learn the robust representations of underwater acoustic target signal denoising and is superior to the traditional methods.
AbstractList	The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference background field in underwater acoustic channels, which brings adverse effects to underwater acoustic target recognition. To improve the recognition accuracy of underwater targets under background noise interference, a bidirectional denoising autoencoder (BDAE) is proposed in this paper for underwater acoustic target signal denoising robust representation learning. The proposed bidirectional denoising autoencoder is an extension of the regular denoising autoencoder, which uses the original underwater acoustic target signals and their corresponding denoised signals to learn robust representations. We then measure the usefulness of the learned representations using a support vector machine classifier. Our proposed approach is verified on the ShipsEar database. Experimental results indicate that the proposed bidirectional denoising autoencoder can effectively learn the robust representations of underwater acoustic target signal denoising and is superior to the traditional methods. The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference background field in underwater acoustic channels, which brings adverse effects to underwater acoustic target recognition. To improve the recognition accuracy of underwater targets under background noise interference, a bidirectional denoising autoencoder (BDAE) is proposed in this article for underwater acoustic target signal denoising robust representation learning. The proposed BDAE is an extension of the regular denoising autoencoder, which uses the original underwater acoustic target signals and their corresponding denoised signals to learn robust representations. We then measure the usefulness of the learned representations using a support vector machine (SVM) classifier. Our proposed approach is verified on the ShipsEar database. Experimental results indicate that the proposed BDAE can effectively learn the robust representations of underwater acoustic target signal denoising and is superior to the traditional methods.
Author	Shen, Xiaohong Dong, Yafen Wang, Haiyan
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SubjectTerms	Background noise bidirectional denoising autoencoder Correlation Interference Learning Noise measurement Noise reduction pseudo clean label representation learning Representations Robustness Signal denoising Support vector machines Surface waves Target recognition Training Underwater acoustic target signal denoising Underwater acoustics
Title	Bidirectional Denoising Autoencoders Based Robust Representation Learning for Underwater Acoustic Target Signal Denoising
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