  We propose a self-supervised learning model to denoise gravitational wave
(GW) signals in the time series strain data without relying on waveform
information. Denoising GW data is a crucial intermediate process for
machine-learning-based data analysis techniques, as it can simplify the model
for downstream tasks such as detections and parameter estimations. We use the
blind-spot neural network and train it with whitened strain data with GW
signals injected as both input data and target. Under the assumption of a
Gaussian noise model, our model successfully denoises 38% of GW signals from
binary black hole mergers in H1 data and 49% of signals in L1 data detected in
the O1, O2, and O3 observation runs with an overlap greater than 0.5. We also
test the model's potential to extract glitch features and loud inspiral compact
binary coalescence signals a few seconds before the merger.
