Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

Yang, Honghui; Shen, Sheng; Yao, Xiaohui; Sheng, Meiping; Wang, Chen

doi:10.3390/s18040952

Cited by 72 publications

(38 citation statements)

References 12 publications

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“…For the application of deep learning, Kamal [10] used a deep belief network and Cao [11] used a stacked autoencoder. A competitive learning mechanism [12,13] was used to increase cluster performance during the training of the deep network. In these works, classifier design and feature extraction were separated from each other.…”

Section: Introductionmentioning

confidence: 99%

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Shen

Yang

Yao

et al. 2020

Sensors

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Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditory features. In the auditory center model, auditory features are first selectively emphasized in a supervised manner. Then, spectro-temporal patterns are extracted by deep architecture with multistage auditory mechanisms. The whole model is optimized with an objective function of ship type classification to form the plasticity of the auditory system. The contributions compared with an auditory inspired convolutional neural network include the improvements in dilated convolutions, deep architecture and target layer. The proposed model can extract auditory features from a raw hydrophone signal and identify types of ships under different working conditions. The model achieved a classification accuracy of 87.2% on four ship types and ocean background noise.

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Section: Introductionmentioning

confidence: 99%

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Shen

Yang

Yao

et al. 2020

Sensors

Self Cite

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“…Ke et al in [16] utilized deep Autoencoder and SVM to classify two classes of underwater acoustic targets. In [17,18], deep belief networks (DBNs) were utilized to the recognition of ship-radiated noise. The deep convolutional neural network (CNN) has been applied to ship-radiated noise recognition in [4].…”

Section: Related Workmentioning

confidence: 99%

Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

Yuan

Cheng

2019

JMSE

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Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.

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“…Therefore, this method is not suitable for extracting the characteristics of the vibration responses of underground structures that cannot avoid noise interference. RBM and its derivative deep belief network [33] use the probability distribution rather than the real-valued sequence to express the characteristics of the hidden layer. These two methods for dimensionality reduction are not suitable for the similarity measure of real-valued sequences.…”

Section: Introductionmentioning

confidence: 99%

Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures

Zuo

et al. 2020

Sensors

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Quantifying structural status and locating structural anomalies are critical to tracking and safeguarding the safety of long-distance underground structures. Given the dynamic and distributed monitoring capabilities of an ultra-weak fiber Bragg grating (FBG) array, this paper proposes a method combining the stacked denoising autoencoder (SDAE) network and the improved dynamic time wrapping (DTW) algorithm to quantify the similarity of vibration responses. To obtain the dimensionality reduction features that were conducive to distance measurement, the silhouette coefficient was adopted to evaluate the training efficacy of the SDAE network under different hyperparameter settings. To measure the distance based on the improved DTW algorithm, the one nearest neighbor (1-NN) classifier was utilized to search the best constraint bandwidth. Moreover, the study proposed that the performance of different distance metrics used to quantify similarity can be evaluated through the 1-NN classifier. Based on two one-dimensional time-series datasets from the University of California, Riverside (UCR) archives, the detailed implementation process for similarity measure was illustrated. In terms of feature extraction and distance measure of UCR datasets, the proposed integrated approach of similarity measure showed improved performance over other existing algorithms. Finally, the field-vibration responses of the track bed in the subway detected by the ultra-weak FBG array were collected to determine the similarity characteristics of structural vibration among different monitoring zones. The quantitative results indicated that the proposed method can effectively quantify and distinguish the vibration similarity related to the physical location of structures.

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Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

Cited by 72 publications

References 12 publications

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures

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