Voice2Series: Reprogramming Acoustic Models for Time Series Classification. (arXiv:2106.09296v3 [cs.LG] UPDATED)

Learning to classify time series with limited data is a practical yet
challenging problem. Current methods are primarily based on hand-designed
feature extraction rules or domain-specific data augmentation. Motivated by the
advances in deep speech processing models and the fact that voice data are
univariate temporal signals, in this paper, we propose Voice2Series (V2S), a
novel end-to-end approach that reprograms acoustic models for time series
classification, through input transformation learning and output label mapping.
Leveraging the representation learning power of …

arxiv classification time time series