End-to-end acoustic target recognition based on 1D convolutional and LSTM networks
Acoustic target recognition plays a crucial role in defense and marine environment monitoring.However,traditional time-frequency domain feature extraction methods often suffer from information loss and inadequate adaptability to varying environments,limiting their recognition performance.To address these limitations,this paper presents an end-to-end acoustic target recognition model(1DLSTM)that combines a one-dimensional convolutional neural network(1D CNN)with a long short-term memory network(LSTM).This model directly processes raw time-domain signals,using the 1D CNN to extract local features and the LSTM to capture long-term dependencies,thereby effectively preserving the global information of the signal.Experimental results on the ShipsEar dataset demonstrate that this model achieves a recognition accuracy of 93.91%,offering a novel approach to end-to-end acoustic target recognition.
deep learningacoustic target recognitionend-to-end
NETL
NSTL
万方数据
