Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion

Abstract Feature fusion methods are introduced to ship-radiated noise recognition in this paper. Wavelet packet (WP) decomposition is used to decompose the ship-radiated noise into multiple different subbands. By considering the features extracted from the different subbands reflecting different characteristics of the ship-radiated noise, a two-dimensional feature fusion (2DFF) scheme is proposed to fuse the features extracted from the different subbands. Principal component analysis (PCA) and canonical correlation analysis (CCA) are used in the 2DFF scheme. Then, a so-called discriminative ability improving (DAI) strategy is proposed to improve the discriminative ability of the extracted features. Starting at the 2DFF, a processing chain of feature fusion and ship-radiated noise recognition is designed and jointly optimized to the task. The 2DFF scheme and DAI strategy are tested on real ship-radiated noise data recorded. Experimental results indicate that compared with the baseline, the 2DFF scheme can improve 7.25% of recognition accuracy. Experimental results also show that the DAI strategy can further improve the recognition accuracy of 13.10%.

[1]  Tae Hong Park,et al.  Not Just More FMS: Taking IT to the Next Level , 2008, ICMC.

[2]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[3]  Antonio Cardenal-Lopez,et al.  ShipsEar: An underwater vessel noise database , 2016 .

[4]  Yan Liu,et al.  A new method of feature fusion and its application in image recognition , 2005, Pattern Recognit..

[5]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[6]  Jürgen Herre,et al.  Robust matching of audio signals using spectral flatness features , 2001, Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics (Cat. No.01TH8575).

[7]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[8]  Yang Yu,et al.  Deep learning-based recognition of underwater target , 2016, 2016 IEEE International Conference on Digital Signal Processing (DSP).

[9]  Christian Igel,et al.  Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Qingxin Meng,et al.  A wave structure based method for recognition of marine acoustic target signals , 2015 .

[11]  Mohamed Abdel-Mottaleb,et al.  Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition , 2016, IEEE Transactions on Information Forensics and Security.

[12]  Theodoros Giannakopoulos,et al.  Introduction to Audio Analysis: A MATLAB® Approach , 2014 .

[13]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[14]  Chen Wang,et al.  Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition , 2018, Sensors.

[15]  Xin Wei On feature extraction of ship radiated noise using 11/2 d spectrum and principal components analysis , 2016 .

[16]  Wang Zhi-qiang,et al.  Underwater Target Recognition Based on Wavelet Packet and Principal Component Analysis , 2011 .

[17]  Di Wu,et al.  Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor , 2016, J. Sensors.

[18]  Amir Averbuch,et al.  Acoustic detection and classification of river boats , 2011 .

[19]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[20]  Jian Liu,et al.  Underwater Target Recognition Based on Line Spectrum and Support Vector Machine , 2014 .

[21]  Qiang Huang,et al.  Underwater target classification using wavelet packets and neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[22]  Xiangyang Zeng,et al.  Robust underwater noise targets classification using auditory inspired time–frequency analysis , 2014 .

[23]  H. Abdi,et al.  Principal component analysis , 2010 .

[24]  Christian Igel,et al.  Hydroacoustic Signal Classification Using Support Vector Machines , 2012 .

[25]  Jian Yang,et al.  Feature fusion: parallel strategy vs. serial strategy , 2003, Pattern Recognit..