Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network
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Author/sNavarro, Juan Miguel; Martínez España, Raquel; Bueno Crespo, Andrés; Martínez, Ramón; Cecilia, José María
Discipline/sIngeniería, Industria y Construcción
Subject/sSmart Wireless Acoustic Sensor Network
Deep neural network
Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict ne...