Authors - Alvaro A. Casanova, Elvis E. Gaona, Oscar Acosta Abstract - This paper introduces a stochastic modeling framework for analyzing binaural urban noise in Bogotá, Colombia. A total of 4,200 five-minute audio recordings were collected using binaural sensors placed at seven urban locations over 47 days. To analyze the data, we utilized cloud-based tools, including Inferencer app for machine-learning-based sound classification and Soundmetrics app for extracting acoustic parameters such as LEQ (equivalent continuous sound level), IACC (inter-aural cross-correlation coefficient), and WIACC (weighted inter-aural cross-correlation coefficient). The probabilistic analysis revealed instances of extreme noise events exceeding 85 dB. We identified correlation patterns that informed the development of a predictive model based on discrete-time Markov chains. Which incorporated both noise intensity and sound source classification into composite acoustic states. The simulated trajectories effectively captured the temporal dynamics of urban acoustic conditions, successfully meeting the study’s objective of modeling future noise dynamics. This approach demonstrates a scalable solution for real-time monitoring, statistical characterization, and predictive modeling of complex urban soundscapes, providing actionable insights that support data-driven decision-making in smart city planning.
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