DLT-GAN: Dual-Layer Transfer Generative Adversarial Network-Based Time Series Data Augmentation Method

In actual production processes, analysis and prediction tasks commonly rely on large amounts of time-series data. However, real-world scenarios often face issues such as insufficient or imbalanced data, severely impacting the accuracy of analysis and predictions. To address this challenge, this paper proposes a dual-layer transfer model based on Generative Adversarial Networks (GANs) aiming to enhance the training speed and generation quality of time-series data augmentation under small-sample conditions while reducing the reliance on large training datasets. This method introduces a module transfer strategy based on the traditional GAN framework which balances the training between the discriminator and the generator, thereby improving the model’s performance and convergence speed. By employing a dual-layer network structure to transfer the features of time-series signals, the model effectively reduces the generation of noise and other irrelevant features, improving the similarity of the generated signals’ characteristics. This paper uses speech signals as a case study, addressing scenarios where speech data are difficult to collect and the limited number of speech samples available for effective feature extraction and analysis. Simulated speech timbre generation is conducted, and the experimental results on the CMU-ARCTIC database show that, compared to traditional methods, this approach achieves significant improvements in enhancing the consistency of generated signal features and the model’s convergence speed.