Fig. 1:

The architecture of the proposed hierarchical attention network. It contains three main components: a) convolutional feature extractor, $G_f\left(\cdot ,{\theta }_f\right)$, b) beat encoder, $G_b\left(\cdot ,{\theta }_b\right)$, and c) window encoder, $G_w\left(\cdot ,{\theta }_w\right)$. The input Doppler signal is divided into windows of 3.75 s $\left(x_1,x_2,\dots ,x_n\right)$. The scalogram of each window is calculated before feeding the network where $ith$ window has time samples $x_{i1},\dots ,x_{iT}$ after the time-frequency feature construction.