  \ac{RAT} classification and monitoring are essential for efficient
coexistence of different communication systems in shared spectrum. Shared
spectrum, including operation in license-exempt bands, is envisioned in the
\ac{5G} standards (e.g., 3GPP Rel. 16). In this paper, we propose a \ac{ML}
approach to characterise the spectrum utilisation and facilitate the dynamic
access to it. Recent advances in \acp{CNN} enable us to perform waveform
classification by processing spectrograms as images. In contrast to other
\ac{ML} methods that can only provide the class of the monitored \acp{RAT}, the
solution we propose can recognise not only different \acp{RAT} in shared
spectrum, but also identify critical parameters such as inter-frame duration,
frame duration, centre frequency, and signal bandwidth by using object
detection and a feature extraction module to extract features from
spectrograms. We have implemented and evaluated our solution using a dataset of
commercial transmissions, as well as in a \ac{SDR} testbed environment. The
scenario evaluated was the coexistence of WiFi and LTE transmissions in shared
spectrum. Our results show that our approach has an accuracy of 96\% in the
classification of \acp{RAT} from a dataset that captures transmissions of
regular user communications. It also shows that the extracted features can be
precise within a margin of 2\%, %of the size of the image, and is capable of
detect above 94\% of objects under a broad range of transmission power levels
and interference conditions.
