###### Abstract  
Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods. ResNet; underwater acoustics; ShipsEar; embedding; SpecAugment; UATR; MFCC; combined features +
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of Mel Frequency Cepstral Coefficients (MFCC) and Log-Mel Spectrogram (LM) are two widely used features in Environment Sound Classification (ESC) tasks [9; 10] with acceptable performance. Although such features originate from the speech or sound field, the effect of MFCC and its first-order differential MFCC or second-order MFCC features are proven for underwater acoustic target recognition [8]. Besides, a considerable number of research works indicate that the fusion feature can give a more comprehensive representation of environment sounds [11]. For the recognition of underwater acoustic targets, [PERSON] et al. [6] exploit the fusion feature of zero-crossing wavelength, peek-to-peek amplitude, and zero-crossing-wavelength difference. On the other hand, the design of the neural networks plays an important role in achieving a competitive performance together with the optimized feature extraction. For example, a time-delay neural network (TDNN) and convolutional neural network (CNN) are introduced for UATR in [12]. [PERSON] et al. [13] present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. The system is based on a computationally and energy-efficient preprocessing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. [PERSON] et al. [14] also have proven that transfer learning can be a viable approach in these scenarios where tagged data is often lacking and evaluate the feasibility of the model based on Recurrent Neural Networks (RNN) for the scenarios requiring on-line processing of the reflection sequence. [PERSON] et al. [15] introduce the auditory inspired convolutional neural networks trained from raw underwater acoustic signal. For ShipsEar [16], the machine learning method using a GMM-based classifier with the standard expectation maximization (EM) algorithm for training purposes could be used as a baseline method, whose best classification rate is 75.4%. As the results obtained by typical methods based on machine learning are not very high, the methods based on deep learning models are worthy of researching. [PERSON] et al. [5] introduce a feature optimization approach with Deep Neural Networks (DNN) and optimizing loss function and achieve an accuracy of 84%. [PERSON] et al. [17] propose a so-called competitive Deep Belief Nets (cDBN) for UATR. [PERSON] et al. [18] present a UATR method based on Restricted Boltzmann Machine (RBM), which achieves the accuracy of 93.17% on the dataset of ShipsEar. [PERSON] et al.