  Because of the small strain amplitudes of gravitational-wave (GW) signals,
unveiling them in the presence of detector/environmental noise is challenging.
For visualizing the signals and extracting its waveform for a comparison with
theoretical prediction, a frequency-domain whitening process is commonly
adopted for filtering the data. In this work, we propose an alternative
template-free framework based on autoregressive modeling for denoising the GW
data and extracting the waveform. We have tested our framework on extracting
the injected signals from the simulated data as well as a series of known
compact binary coalescence (CBC) events from the LIGO data. Comparing with the
conventional whitening procedure, our methodology generally yields improved
cross-correlation and reduced root mean square errors with respect to the
signal model.
