  Gravitational wave astronomy is a rapidly growing field of modern
astrophysics, with observations being made frequently by the LIGO detectors.
Gravitational wave signals are often extremely weak and the data from the
detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary
noise, often containing transient disturbances which can obscure real signals.
Traditional denoising methods, such as principal component analysis and
dictionary learning, are not optimal for dealing with this non-Gaussian noise,
especially for low signal-to-noise ratio gravitational wave signals.
Furthermore, these methods are computationally expensive on large datasets. To
overcome these issues, we apply state-of-the-art signal processing techniques,
based on recent groundbreaking advancements in deep learning, to denoise
gravitational wave signals embedded either in Gaussian noise or in real LIGO
noise. We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder,
based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent
neural networks. We demonstrate the advantages of using our unsupervised deep
learning approach and show that, after training only using simulated Gaussian
noise, SMTDAE achieves superior recovery performance for gravitational wave
signals embedded in real non-Gaussian LIGO noise.
