A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.
Background: In the ecosystem, birds are an important component, which is crucial for regulating the ecological environment and monitoring biodiversity, and can even assist in predicting natural disasters such as earthquakes and tsunamis by monitoring the movement of birds and listening to their abnormal calls, so bird sound recognition and abnormal call detection have become popular research directions. However, low recognition rate is caused to the problems of insufficient feature extraction in traditional bird sound recognition methods. Method: In this paper, we used a fusion feature method combined with deep learning to extract bird sound features. The fusion features were obtained by splicing the original signal parameters with the modified log-Meier spectral difference parameters; the deep learning method was based on the DenseNet121 network structure and incorporated the self-attention module and the central loss function for bird sound recognition. The self-attentive module partially improved the feature representation of key channels; the central loss function was used to solve the problem of incompact intra-class features. We used the data of 10 bird sounds from the Xeno-Canto World Wild Bird Sounds public dataset to test the accuracy of bird chirp recognition. Conclusion:In this paper, a neural network structure containing self-attention mechanism and center loss function is proposed for bird song recognition. Its verification accuracy reaches to 96.9%. The code is open source to Github: https://github.com/ CarrieX6/-Xeno-Canto-.git.
IntroductionVarious types of ships sail at sea, and identifying maritime ship types through shipradiated noise is one of the tasks of ocean observation. The ocean environment is complex and changeable, such rapid environmental changes underline the difficulties of obtaining a huge amount of samples. Meanwhile, the length of each sample has a decisive influence on the classification results, but there is no universal sampling length selection standard.MethodsThis study proposes an effective framework for ship-radiated noise classification. The framework includes: i) A comprehensive judgment method based on multiple features for sample length selecting. ii) One-dimensional deep convolution generative adversarial network (1-DDCGAN) model to augment the training datasets for small sample problem. iii) One-dimensional convolution neural network (CNN) trained by generated data and real data for ship-radiated noise classification. On this basis, a onedimensional residual network (ResNet) is designed to improve classification accuracy.ResultsExperiments are performed to verify the proposed framework using public datasets. After data augmentation, statistical parameters are used to measure the similarity between the original samples and the generated samples. Then, the generated samples are integrated into the training set. The convergence speed of the network is clearly accelerated, and the classification accuracy is significantly improved in the one-dimensional CNN and ResNet.DiscussionIn this study, we propose an effective framework for the lack of scientific sample length selection and lack of sample number in the classification of ship-radiated noise, but there aret still some problems: high complexity, structural redundancy, poor adaptability, and so on. They are also long-standing problems in this field that needs to be solved urgently.
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