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《Journal of Applied Acoustics》 2019-06
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Research on manifold learning in acoustic feature extraction of moving sound source

SU Yuanliang;LIU Zhihong;WANG Wankai;ZHAO Yugui;YI Chuijie;College of Mechanical and Automotive Engineering, Qingdao University of Technology;Key Laboratory of Energy Conservation and Pollution Control of Industrial Fluids, Ministry of Education;  
Moving noise is characterized by time-varying, superimposing and space-time coupling of sound signals, and the sound data are characterized by high dimensionality and nonlinearity, which makes it difficult to extract key acoustic features. The method of sound feature extraction has high complexity, large numerical calculation and poor validity. Therefore, how to effectively extract acoustic features and reduce the complexity of the extraction method has become an important scientific problem for the accurate identification of multisource acoustic sources. In this paper, the STFT-LLE manifold learning method is proposed. It combined with short-time Fourier transform(STFT) and local linear embedding algorithm(LLE). This method is applied to the feature extraction of motion acoustic field. It is validated by simulation experiments. This method provides technical support for the classification and recognition of moving sound source.
【Fund】: 国家自然科学基金项目(61671262 61871447)
【CateGory Index】: TP181;TN912.3
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