Crossterm-free time-frequency representation exploiting deep convolutional neural network

•We developed a deep neural network-based approach to generate crossterm-free time-frequency representations.•We show that, provided that the neural networks are adequately trained, the proposed method works robustly and provides significant performance improvement compared to existing time-frequency representation reconstruction algorithms.•We evaluated the generalization capability of the proposed method, including the effects of noise levels, amplitude difference, variation speed of the IFs, fading, number of signal components, and frequency spreading.