Intelligent recognition of underwater acoustic targets is crucial for exploiting marine resources. In this paper, we propose a deep learning method for underwater acoustic signal recognition consisting of three steps: Firstly, we extract the underwater acoustic signal's identification vector (i-vector) using a Gaussian mixture model. Secondly, we construct a probabilistic linear discriminant analysis (PLDA) model that divides the i-vector into two segments, Finally, we used a multi-long short-term memory neural network (MLSTMNN) to learn the features, outputting the underwater acoustic signal labels through the softmax layer. Experiments using the ShipsEar dataset can obtain a recognition rate of 84.8%; compared to the traditional Mel frequency Cepstral Coefficients (MFCC) and convolutional neural networks, the recognition rate greatly improved.
PLDA in i-vector based underwater acoustic signals classification
SHIPS AND OFFSHORE STRUCTURES
Y. SONG ET AL.
Ships and Offshore Structures ; 19 , 3 ; 366-374
2024-03-03
9 pages
Article (Journal)
Electronic Resource
English
UNDERWATER ACOUSTIC VECTOR SENSOR DEVELOPMENT AND APPLICATIONS
British Library Online Contents | 2011
|Cancellation techniques in underwater scattering of acoustic signals
Online Contents | 2004
|Vehicle Classification Using Networks Based Upon Acoustic Signals
British Library Conference Proceedings | 1995
|A laser sensing scheme for detection of underwater acoustic signals
Tema Archive | 1988
|