流形学习在运动声源声特征提取方面的研究*

运动声源因声信号时变性、叠加性和空时耦合性强,声数据呈现高维、非线性等特点,使得关键声特征提取困难,声特征提取方法复杂度高、数值计算量大、有效性差。因此,如何有效提取声特征并降低提取方法复杂度成为目前多源声场声源精准识别需迫切解决的关键科学问题。由此,该文提出短时傅里叶变换(STFT)和局部线性嵌入算法(LLE)联合的STFT-LLE流形学习声特征提取方法,并将此方法应用于运动声特征提取,且通过仿真实验测试对其进行了验证。该方法为运动声目标的分类识别提供了技术支撑。

Research on manifold learning in acoustic feature extraction of moving sound source

Moving noise is characterized by time-varying, superimposing and space-time coupling of sound signals, and the sound data is 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 multi-source 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 acousticfield. It is validated by simulation experiments.