Creating a specialized dataset for enhanced noise compliance in live concert environments

Regulatory guidelines mandate strict noise levels during concerts, specially for night leisure soundscapes, yet accurate compliance assessment is hindered by the presence of extraneous noises. This paper presents a methodology focused on creating a specialized dataset tailored to address this challenge. We have captured several live concerts, ensuring the dataset's relevance and applicability in noise compliance assessments. This paper exposes the methodology followed since the deployment of the sensors until the labeling of the most relevant parts of it by using the LAeq levels to select the most prominent sound sources. The dataset has been used to use a PANN and incorporate a classification mechanism into a noise monitoring platform for live concerts. By leveraging LAeq levels to identify prominent sound sources, our methodology ensures the inclusion of key acoustic events in the dataset, facilitating subsequent analysis and classification tasks. The integration of a PANN (Pretrained Audio Neural Network) enables the development of a robust classification mechanism tailored to the intricacies of live concert environments. This work underscores the importance of specialized datasets in addressing the challenges of noise compliance in concert settings, offering a modest resource for advancing research and practical applications in this field.