  The matched filtering paradigm is the mainstay of gravitational wave (GW)
searches from astrophysical coalescing compact binaries. The compact binary
coalescence (CBC) search pipelines perform the matched filter between the GW
detector's data and a large set of analytical waveforms. However, the
computational cost of performing matched filter is very high as the required
number of the analytical waveforms is also high. Recently, various deep
learning-based methods have been deployed to identify a GW signal in the
detector output as an alternative to computationally expensive matched
filtering techniques. In past work, the researchers have considered the
detection of GW signal mainly as a classification problem, in which they train
the deep learning-based architecture by considering the noise and the GW signal
as two different classes. However, in this work, for the first time, we have
combined the Convolutional Neural Network (CNN) and matched filter methods to
reduce the computational cost of the search by reducing the number of matched
filtering operations. We have implemented the CNN based architecture not only
for classification of the signal but also to identify the location of the
signal in the intrinsic parameter space. Identifying the location in which the
detected signal lies enables us to perform the matched filter operations
between the data and the analytical waveforms generated for the smaller region
of the parameter space only - thereby reducing the computational cost of the
search. We demonstrate our method for two-dimensional parameter space for
stellar to high mass binary black hole systems. In particular, we are able to
classify between pure noise and noisy BBH signals with 99% accuracy. Further,
the detected signals have been sub-classified into patches in mass components
with an average accuracy > 97%
