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

Abstract

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.