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Spectrograms are frequently used to provide qualitative insights into the types of noise and signals present in audio data. Similarly, we can use them to gain insights from data such as real gravitational wave from gravitational wave detectors. Simply by eye, we can see characteristic chirp signals from gravitational waves due to the physics of the black holes' inspiral. Designing a novel machine learning model named SpectroGW, which is based on a convolutional neural network, we can determine if spectrograms containing gravitational wave signals does in fact possess qualitative characteristics. The model can be modified to predict the masses of the merging bodies. We can utilize fast Fourier transforms and amplitude spectral density can be used to characterise the changes in the data across time in the frequency domain to bring out the feature of the binary black hole merger's chirp. Additionally, we can identify the features and types of noise present in the gravitational wave data.
