Internet of Things-based computation of classroom acoustic parameters and estimation of speech intelligibility

Abstract

This paper presents an Internet of Things-based system designed to measure classroom acoustic parameters and estimate speech intelligibility in real time. The system consists of multiple wireless sensor nodes equipped with microphones that capture acoustic data and transmit them to a central server for processing. Parameters such as reverberation time (RT60) and the Speech Intelligibility Index were calculated to assess acoustic quality. Experiments were conducted in a university lecture hall under different sound source configurations to evaluate the system's performance. Results indicate that the proposed IoT-based approach effectively characterizes acoustic conditions and provides a cost-efficient alternative to traditional measurement tools, with potential applications in classroom design, audio optimization, and smart learning environments.