Signal-noise separation using unsupervised reservoir computing
This provides a method for signal-noise separation in various applications, but it is incremental as it builds on existing reservoir computing techniques.
The paper tackled the problem of separating signal from noise without prior knowledge of noise characteristics by using unsupervised reservoir computing for time series prediction, achieving robust performance even for signals with strong noise and negative signal-to-noise ratios.
Removing noise from a signal without knowing the characteristics of the noise is a challenging task. This paper introduces a signal-noise separation method based on time series prediction. We use Reservoir Computing (RC) to extract the maximum portion of "predictable information" from a given signal. Reproducing the deterministic component of the signal using RC, we estimate the noise distribution from the difference between the original signal and reconstructed one. The method is based on a machine learning approach and requires no prior knowledge of either the deterministic signal or the noise distribution. It provides a way to identify additivity/multiplicativity of noise and to estimate the signal-to-noise ratio (SNR) indirectly. The method works successfully for combinations of various signal and noise, including chaotic signal and highly oscillating sinusoidal signal which are corrupted by non-Gaussian additive/ multiplicative noise. The separation performances are robust and notably outstanding for signals with strong noise, even for those with negative SNR.