Research on feature extraction and classification algorithm of Marine environmental noise based on deep learning

In the field of feature extraction and classification of Marine environmental noise, an innovative research work deeply integrates Long Short-Term Memory (LSTM) neural network with Variational Quantum Algorithms (VQA). This builds a new hybrid model known as Long Short Quantum Algorithms Memory (LSQAM). The aim of this research is to solve the problem of feature extraction and classification of high-dimensional and nonlinear noise signals in complex Marine environments. As a special type of recurrent neural network, LSTM has significant advantages in processing sequence data and capturing time-dependent relationships, and is especially suitable for analyzing Marine environmental noise signals containing long-term correlation and dynamic change characteristics. Through its unique gating mechanism, LSTM is able to efficiently store long-term information and filter out irrelevant interference, thereby extracting key spatio-temporal features from noisy signals. However, in the face of increasing data dimensions and highly complex noise patterns, traditional LSTM models may be limited by low computational efficiency and insufficient model generalization ability. To overcome these challenges, researchers introduce variable component subalgorithms. By making full use of the parallel computing power and quantum entanglement effect of quantum computers, VQA can significantly improve the solving speed of specific optimization problems, and show unique advantages for processing large-scale high-dimensional data. LSQAM model cleverly combines the advantages of LSTM and VQA. LSTM is responsible for extracting and capturing important local and global spatiotemporal features from the original ocean noise signals. Then, variable component subcircuits are used to encode and compress the high-dimensional features extracted by LSTM efficiently. At the same time, the feature space is deeply explored and optimized by combining quantum optimization algorithms to improve the learning efficiency and generalization performance of the model in the task of noise source classification. Practice has proved that the LSQAM model has successfully realized the in-depth mining and accurate classification of Marine environmental noise characteristics, which not only improves the accuracy of noise source identification, but also greatly reduces the need for computing resources. This novel approach of integrating artificial intelligence and quantum computing technology marks a strong combination of the two in practical problem solving, and has great significance for promoting technological innovation in many fields, such as Marine scientific research, environmental monitoring, and military applications. With the development of quantum computing hardware technology and theoretical research, LSQAM model is expected to play a more critical role in the future large-scale and more complex noise recognition scenarios, leading the study of Marine environmental noise into a new stage of development.