Passive recognition of underwater acoustic targets is a hot research issue in acoustic signal processing. The long-term interference of irregular noise in the marine environment caused the relevance of the passive recognition method of underwater targets based on the traditional technical framework to gradually decrease. Due to the interference of irregular noise in the ocean, the passive recognition method used for underwater targets based on the traditional technical framework is gradually becoming less relevant. The feature extraction method that combines deep learning and time–frequency spectrogram can better describe the differences of different targets. In this paper, the proposed model contains three steps to deal with the recognition of underwater targets: feature extraction, data augmentation and deep neural network. For the feature extraction, we use a Mel-spectrogram, as well as the delta and delta-delta features in order to construct 3-D features. In the data augmentation part, we expand the dataset with SpecAugment in the time domain and frequency domain. In deep neural network prediction part, we use the convolutional recurrent neural network (CRNN) for acoustic target recognition. Through a comparison with the ablation test, it is clear that the pipeline in our method is effective in acquiring the recognition result. After evaluating our system through the carrying out of three tasks on the ShipsEar dataset, and the recognition accuracy are 94.6%, 87.5% and 72.6% in task 1, task 2 and task 3 respectively.

水下声目标的被动识别是声信号处理中的一个热门研究问题。海洋环境中不规则噪声的长期干扰导致基于传统技术框架的水下目标被动识别方法的相关性逐渐降低。由于海洋中不规则噪声的干扰，基于传统技术框架的水下目标被动识别方法正变得越来越不相关。结合深度学习和时频频谱图的特征提取方法可以更好地描述不同目标的差异。在本文中，提出的模型包含三个步骤来处理水下目标的识别：特征提取，数据增强和深度神经网络。对于特征提取，我们使用Mel频谱图以及delta和delta-delta特征来构建3-D特征。在数据扩充部分，我们在时域和频域中使用SpecAugment扩展数据集。在深度神经网络预测部分，我们使用卷积递归神经网络（CRNN）进行声学目标识别。通过与消融测试的比较，很明显，我们方法中的管道可以有效地获得识别结果。通过在ShipsEar数据集上执行三个任务对我们的系统进行评估，任务1，任务2和任务3的识别准确度分别为94.6％，87.5％和72.6％。我们在时域和频域中使用SpecAugment扩展数据集。在深度神经网络预测部分，我们使用卷积递归神经网络（CRNN）进行声学目标识别。通过与消融测试的比较，很明显，我们方法中的管道可以有效地获得识别结果。通过在ShipsEar数据集上执行三个任务对我们的系统进行评估，任务1，任务2和任务3的识别准确度分别为94.6％，87.5％和72.6％。我们在时域和频域中使用SpecAugment扩展数据集。在深度神经网络预测部分，我们使用卷积递归神经网络（CRNN）进行声学目标识别。通过与消融测试的比较，很明显，我们方法中的管道可以有效地获得识别结果。通过在ShipsEar数据集上执行三个任务对我们的系统进行评估，任务1，任务2和任务3的识别准确度分别为94.6％，87.5％和72.6％。显然，我们方法中的流水线有效地获得了识别结果。通过在ShipsEar数据集上执行三个任务对我们的系统进行评估，任务1，任务2和任务3的识别准确度分别为94.6％，87.5％和72.6％。显然，我们方法中的流水线有效地获得了识别结果。通过在ShipsEar数据集上执行三个任务对我们的系统进行评估，任务1，任务2和任务3的识别准确度分别为94.6％，87.5％和72.6％。