  Deep neural networks (DNNs) that tackle the time series classification (TSC)
task have provided a promising framework in signal processing. In real-world
applications, as a data-driven model, DNNs are suffered from insufficient data.
Few-shot learning has been studied to deal with this limitation. In this paper,
we propose a novel few-shot learning framework through data augmentation, which
involves transformation through the time-frequency domain and the generation of
synthetic images through random erasing. Additionally, we develop a
sequence-spectrogram neural network (SSNN). This neural network model composes
of two sub-networks: one utilizing 1D residual blocks to extract features from
the input sequence while the other one employing 2D residual blocks to extract
features from the spectrogram representation. In the experiments, comparison
studies of different existing DNN models with/without data augmentation are
conducted on an amyotrophic lateral sclerosis (ALS) dataset and a wind turbine
fault (WTF) dataset. The experimental results manifest that our proposed method
achieves 93.75% F1 score and 93.33% accuracy on the ALS datasets while 95.48%
F1 score and 95.59% accuracy on the WTF datasets. Our methodology demonstrates
its applicability of addressing the few-shot problems for time series
classification.
