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.


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    Title :

    PLDA in i-vector based underwater acoustic signals classification


    Additional title:

    SHIPS AND OFFSHORE STRUCTURES
    Y. SONG ET AL.


    Contributors:

    Published in:

    Publication date :

    2024-03-03


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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