Signal-noise separation using unsupervised reservoir computing
Jaesung Choi Pilwon Kim
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.