Voice2Series: Reprogramming Acoustic Models for Time Series Classification

• URL: http://arxiv.org/abs/2106.09296v1
• Date: Thu, 17 Jun 2021 07:59:15 GMT
• Title: Voice2Series: Reprogramming Acoustic Models for Time Series Classification
• Authors: Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen
• Abstract summary: Voice2Series is a novel end-to-end approach that reprograms acoustic models for time series classification. We show that V2S either outperforms or is tied with state-of-the-art methods on 20 tasks, and improves their average accuracy by 1.84%.
• Score: 65.94154001167608
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 a large-scale pre-trained speech processing model, on 30 different time series tasks we show that V2S either outperforms or is tied with state-of-the-art methods on 20 tasks, and improves their average accuracy by 1.84%. We further provide a theoretical justification of V2S by proving its population risk is upper bounded by the source risk and a Wasserstein distance accounting for feature alignment via reprogramming. Our results offer new and effective means to time series classification.

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