  Recognizing underwater targets from acoustic signals is a challenging task
owing to the intricate ocean environments and variable underwater channels.
While deep learning-based systems have become the mainstream approach for
underwater acoustic target recognition, they have faced criticism for their
lack of interpretability and weak generalization performance in practical
applications. In this work, we apply the class activation mapping (CAM) to
generate visual explanations for the predictions of a spectrogram-based
recognition system. CAM can help to understand the behavior of recognition
models by highlighting the regions of the input features that contribute the
most to the prediction. Our explorations reveal that recognition models tend to
focus on the low-frequency line spectrum and high-frequency periodic modulation
information of underwater signals. Based on the observation, we propose an
interpretable contrastive learning (ICL) strategy that employs two encoders to
learn from acoustic features with different emphases (line spectrum and
modulation information). By imposing constraints between encoders, the proposed
strategy can enhance the generalization performance of the recognition system.
Our experiments demonstrate that the proposed contrastive learning approach can
improve the recognition accuracy and bring significant improvements across
various underwater databases.
