Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals
arXiv:2604.03985v1 Announce Type: new Abstract: 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 training. The performance is evaluated through waveform reconstruction and parameter-estimation accuracy. We find that the proposed method can estimate the parameters with high accuracy even in challenging setups, such as those involving a subdominant c
arXiv:2604.03985v1 Announce Type: new Abstract: 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 training. The performance is evaluated through waveform reconstruction and parameter-estimation accuracy. We find that the proposed method can estimate the parameters with high accuracy even in challenging setups, such as those involving a subdominant component or nearly opposite-phase components, while remaining reasonably robust when the training distribution is less informative. This demonstrates its potential as a tool for analyzing short-duration, noisy signals.
Executive Summary
This article presents an autoencoder-based method for estimating parameters of superposed multi-component damped sinusoidal signals. The proposed method utilizes the latent space to accurately estimate frequency, phase, decay time, and amplitude of each component in noisy signals. The approach demonstrates high accuracy and robustness, even in challenging setups such as subdominant components or nearly opposite-phase components. The method is trained on Gaussian-distribution data and also evaluated on uniform distribution data, showing reasonable robustness when the training distribution is less informative. The article contributes to the analysis of short-duration, noisy signals in various physical systems, providing a potential tool for researchers and practitioners.
Key Points
- ▸ Autoencoder-based method for parameter estimation of damped sinusoidal signals
- ▸ Utilizes latent space to estimate frequency, phase, decay time, and amplitude
- ▸ High accuracy and robustness in challenging setups
Merits
Strength in Challenging Setups
The proposed method demonstrates high accuracy and robustness in setups with subdominant components or nearly opposite-phase components, making it a valuable tool for analyzing complex signals.
Robustness to Training Distribution
The method shows reasonable robustness when trained on less informative data distributions, making it a practical choice for applications where data is limited or noisy.
Demerits
Limited to Gaussian-Distribution Training
The method is primarily trained on Gaussian-distribution data, which may limit its applicability to real-world scenarios where data distributions may be non-Gaussian.
Potential Overfitting
The autoencoder-based method may be prone to overfitting, especially when trained on small datasets, which can result in poor generalization performance.
Expert Commentary
This article presents a novel approach to parameter estimation of damped sinusoidal signals using autoencoder-based methods. The proposed method demonstrates high accuracy and robustness, making it a valuable tool for researchers and practitioners. However, the method's limitations, such as its reliance on Gaussian-distribution training and potential overfitting, require further investigation. The article's findings have significant implications for signal processing and analysis, particularly in the context of machine learning and deep learning. As the field of signal processing continues to evolve, the development of new methods and techniques, such as the one presented in this article, will play an increasingly important role in advancing our understanding of complex systems and phenomena.
Recommendations
- ✓ Future research should focus on exploring the method's applicability to non-Gaussian data distributions and developing techniques to mitigate overfitting.
- ✓ The proposed method should be further evaluated on real-world datasets to assess its practicality and performance in various applications.
Sources
Original: arXiv - cs.LG
