  We evaluate several neural-network architectures, both convolutional and
recurrent, for gravitational-wave time-series feature extraction by performing
point parameter estimation on noisy waveforms from binary-black-hole mergers.
We build datasets of 100,000 elements for each of four different waveform
models (or approximants) in order to test how approximant choice affects
feature extraction. Our choices include \texttt{SEOBNRv4P} and
\texttt{IMRPhenomPv3}, which contain only the dominant quadrupole emission
mode, alongside \texttt{IMRPhenomPv3HM} and \texttt{NRHybSur3dq8}, which also
account for high-order modes. Each dataset element is injected into detector
noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK)
collaboration. We identify the Temporal Convolutional Network (TCN)
architecture as the overall best performer in terms of training and validation
losses and absence of overfitting to data. Comparison of results between
datasets shows that the choice of waveform approximant for the creation of a
dataset conditions the feature extraction ability of a trained network. Hence,
care should be taken when building a dataset for the training of neural
networks, as certain approximants may result in better network convergence of
evaluation metrics. However, this performance does not necessarily translate to
data which is more faithful to numerical relativity simulations. We also apply
this network on actual signals from LVK runs, finding that its
feature-extracting performance can be effective on real data.
