  Denoising of time domain data is a crucial task for many applications such as
communication, translation, virtual assistants etc. For this task, a
combination of a recurrent neural net (RNNs) with a Denoising Auto-Encoder
(DAEs) has shown promising results. However, this combined model is challenged
when operating with low signal-to-noise ratio (SNR) data embedded in
non-Gaussian and non-stationary noise. To address this issue, we design a novel
model, referred to as 'Enhanced Deep Recurrent Denoising Auto-Encoder'
(EDRDAE), that incorporates a signal amplifier layer, and applies curriculum
learning by first denoising high SNR signals, before gradually decreasing the
SNR until the signals become noise dominated. We showcase the performance of
EDRDAE using time-series data that describes gravitational waves embedded in
very noisy backgrounds. In addition, we show that EDRDAE can accurately denoise
signals whose topology is significantly more complex than those used for
training, demonstrating that our model generalizes to new classes of
gravitational waves that are beyond the scope of established denoising
algorithms.

Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Machine Learning;Signal Processing;General Relativity and Quantum Cosmology