  Blip glitches, a type of short-duration noise transient in the LIGO--Virgo
data, are a nuisance for the binary black hole (BBH) searches. They affect the
BBH search sensitivity significantly because their time-domain morphologies are
very similar, and that creates difficulty in vetoing them. In this work, we
construct a deep-learning neural network to efficiently distinguish BBH signals
from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are
projections of GW frequency-domain data snippets on a basis of sine-Gaussians
defined by the quality factor and central frequency. We feed the SGP maps to
our deep-learning neural network, which classifies the BBH signals and blips.
Whereas the BBH signals are simulated, the blips used are taken from real data
throughout our analysis. We show that our network significantly improves the
identification of the BBH signals in comparison to the results obtained using
traditional-$\chi^2$ and sine-Gaussian $\chi^2$. For example, our network
improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs
with total mass in the range $[80,140]~M_{\odot}$ and SNR in the range $[3,8]$.
Also, it correctly identifies 95% of the real GW events in GWTC-3. The
computation time for classification is a few minutes for thousands of SGP maps
on a single core. With further optimisation in the next version of our
algorithm, we expect a further reduction in the computational cost. Our
proposed method can potentially improve the veto process in the LIGO--Virgo GW
data analysis and conceivably support identifying GW signals in low-latency
pipelines.
