Sound Levels Forecasting in an Acoustic Sensor Network Using a 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 longterm. Therefore, forecasting of temporal shortterm sound levels could be a helpful tool for urban planners and managers. In this work, a Long ShortTerm 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 neartime future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, oneminute period equivalent sound levels, captured in a twomonth measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statisticsbased models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network Abstract   Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction.xc2xa0Accurate prediction of AP helps people to plan for theirxc2xa0outdoor activities and aids protecting human health. In this paper, longshort term memory (LSTM) recurrent neural networks (RNNs) have been used toxc2xa0predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air qualityxc2xa0monitoring stations (AQMSs) have less observed data in quantity,xc2xa0and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pretrained neuralxc2xa0networks have been employed to assist AQMSs with less observed data toxc2xa0build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledgebased system. As shownxc2xa0by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorterxc2xa0training time than randomly initialized recurrent neural networks.