This repository presents a spline-based method for synthetic signal generation, designed to overcome the limitations of traditional and GAN-based approaches. The method allows precise control over frequency, amplitude, phase, and noise levels, offering a flexible and powerful approach for signal modeling.
Key features of this method include:
- Non-uniform partitioning and spline interpolation to generate signals that are both statistically consistent and adaptable to various experimental needs.
- Robust spectral stability analysis and noise evaluation to assess the quality and reliability of the generated signals.
- Successful application in neural network-based signal reconstruction tasks, demonstrating the versatility of the method.
This work provides a reproducible and scalable framework for synthetic data generation. The generated signals have been successfully applied in diverse fields, including:
- Biomedical analysis
- Financial forecasting
- Industrial monitoring
The repository includes the implementation of this method, along with example code and datasets, making it easy to integrate and apply in your own projects.