The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages of the proposed technique is that it does not require any prior knowledge on the signal, its components, or noise, but rather the processing is performed on the noisy signal mixtures. Also, it is shown that the method is robust to the selection of time-frequency distributions (TFDs). It has been tested for different signal-to-noise-ratios (SNRs), both for synthetic and real-life data. When compared to fixed TFD thresholding, adaptive TFD thresholding based on RICI rule and the 1D entropy-based approach, the proposed adaptive method significantly increases classification accuracy (by up to 11.53%) and F1 score (by up to 7.91%). Hence, this adaptive, data-driven, entropy-based technique is an efficient tool for extracting useful information from noisy data in the time-frequency domain.

本文提出了一种新颖的方法，用于在时频域中提取有用信息并从噪声数据中分离信号成分的盲源。该方法基于在自适应数据驱动的2D区域内部计算的局部Rényi熵，其大小是使用改进的相对置信区间相交（RICI）算法计算的。所提出的技术的优点之一是，它不需要任何有关信号，其分量或噪声的先验知识，而是对有噪声的信号混合物进行处理。而且，表明该方法对于时频分布（TFD）的选择是鲁棒的。已针对合成数据和实际数据测试了不同的信噪比（SNR）。与固定的TFD阈值相比，自适应TFD阈值基于RICI规则和基于1D熵的方法，所提出的自适应方法显着提高了分类准确性（高达11.53％）和F1得分（高达7.91％）。因此，这种自适应的，数据驱动的，基于熵的技术是从时频域中的噪声数据中提取有用信息的有效工具。