Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals
Damped sinusoidal oscillations are widely observed in many physical systems, and their analysis provides access to underlying physical properties. However, parameter estimation becomes difficult when the signal decays rapidly, multiple components are superposed, and observational noise is present. In this study, we develop an autoencoder-based method that uses the latent space to estimate the frequency, phase, decay time, and amplitude of each component in noisy multi-component damped sinusoidal signals. We investigate multi-component cases under Gaussian-distribution training and further examine the effect of the training-data distribution through comparisons between Gaussian and uniform...
| Authors | Momoka Iida, Hayato Motohashi, Hirotaka Takahashi |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 13 September 2026 |
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