  Time series classification(TSC) has always been an important and challenging
research task. With the wide application of deep learning, more and more
researchers use deep learning models to solve TSC problems. Since time series
always contains a lot of noise, which has a negative impact on network
training, people usually filter the original data before training the network.
The existing schemes are to treat the filtering and training as two stages, and
the design of the filter requires expert experience, which increases the design
difficulty of the algorithm and is not universal. We note that the essence of
filtering is to filter out the insignificant frequency components and highlight
the important ones, which is similar to the attention mechanism. In this paper,
we propose an attention mechanism that acts on spectrum (SAM). The network can
assign appropriate weights to each frequency component to achieve adaptive
filtering. We use L1 regularization to further enhance the frequency screening
capability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of
time domain information caused by using the spectrum of the whole sequence. In
which, a tumbling window is introduced to segment the original data. Then SAM
is applied to each segment to generate new features. We propose a heuristic
strategy to search for the appropriate number of segments. Experimental results
show that SSAM can produce better feature representations, make the network
converge faster, and improve the robustness and classification accuracy.
