Adaptive multi-scale TF-net for high-resolution time-frequency representations

A novel adaptive multi-scale time-frequency network (AMTFN) is proposed to provide high-resolution time frequency representations for nonstationary signals. AMTFN is an end-to-end deep network, which firstly adaptively learns the comprehensive basis functions to produce time-frequency (TF) feature maps through multi-scale 1D convolutional kernels. Then, the channel attention mechanism is embedded into AMTFN to rescale the TF feature maps selectively. Thus, the subsequent residual encoder-decoder block's energy concentration performance is greatly improved with these rescaled TF feature maps. Besides, this paper designs a new training strategy to elegantly enable the model to pay more attention to the intersections of instantaneous frequency trajectories. In the end, a series of simulations as well as real-world cases, are studied to demonstrate the effectiveness and advantages of the proposed method.