A deep neural network (DNN)-based method is proposed, which incorporates a blade-vortex interaction (BVI) aeroacoustic model and the improved Mallat-Zhong discrete wavelet transform (MZ-DWT) analysis, to detect and extract the BVI) signal. First, the optimal scale (OPS) and optimal scale vector (OPSV) features are defined based on the improved MZ-DWT to capture the dominant information of the BVI signal. Then, two types of deep neural network-based scale feature models (DNN-SFMs) are designed and trained to automatically obtain the OPS and OPSV features directly from the waveforms of the BVI signals. Finally, with the obtained OPS and OPSV features, a single-scale detector, multi-scale detector, single-scale extractor, and multi-scale extractor are derived for the BVI signal. The results of extensive experiments (BVI signals containing different types of noises are tested with each type of signal consisting of 10 000 or 9000 samples at each signal-to-noise ratio) demonstrate that the proposed detectors and extractors improve the accuracy and robustness of detection and extraction, respectively, and compared to the existing methods, the computational complexity is greatly reduced.

中文翻译：

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基于深度神经网络的尺度特征模型下叶片-涡流相互作用检测与提取

提出了一种基于深度神经网络 (DNN) 的方法，该方法结合了叶片涡相互作用 (BVI) 气动声学模型和改进的 Mallat-Zhong 离散小波变换 (MZ-DWT) 分析，以检测和提取 BVI) 信号。首先，基于改进的 MZ-DWT 定义最优尺度 (OPS) 和最优尺度矢量 (OPSV) 特征，以捕获 BVI 信号的主导信息。然后，设计并训练了两种基于深度神经网络的尺度特征模型（DNN-SFM），以直接从 BVI 信号的波形中自动获取 OPS 和 OPSV 特征。最后，利用获得的OPS和OPSV特征，推导出BVI信号的单尺度检测器、多尺度检测器、单尺度提取器和多尺度提取器。