In association football, the development of multimodal summaries is of great importance to both broadcasters and spectators since a large number of viewers choose to follow just the soccer game highlights. The fundamental drive for the development of summarization systems is the requirement to manage huge amounts of data in different formats. By highlighting the most pertinent facts and limiting or omitting unnecessary aspects, summarization helps avoid "information overload." The properties of the audio signals during a particular event can be used to calculate excitement around that event and filter events based on their importance. A root-mean-square (RMS) analysis of audio events was carried out to analyse the excitement across the events in the SoccerNet dataset. It was clearly seen that important events with excitement have a high and distinguishable RMS audio intensity. It was also observed that the generated noise of the crowd was significantly different across various events and if it happened for the home or away team. The intensity was higher for events related to the home team. Likewise, as the wavelet has the benefit of integrating a wave with a specific period, Morlet wavelet analysis was performed for various event types, and the power of the signal across various wavelet scales was analyzed. A distinct signature across various wavelet scales was observed for different events.