Abstract: A generation system (100) of synthesized sound comprises: a first stage (1), wherein features (F) are extracted from an input raw sound and parameters of said features are evaluated; a second stage (2) wherein the evaluated parameters are used to create a plurality of physical models that are metrically evaluated in order to find the parameters of the best physical model, and a third stage (3) wherein the parameters of the best physical model are perturbed in order to create perturbed physical models and a metric evaluated of the perturbed physical models is performed to find the parameters of the best physical model.

Abstract: A system for monitoring an acoustic scene outside a vehicle; the system including: a vehicle with wheels and a trunk, an acoustic sensor disposed in the trunk, a control unit operatively connected to the acoustic sensor, and at least one neural network operatively connected to the control unit, and trained in such a way to correlate the characteristics of an audio signal with types of road surfaces; the control unit is configured in such a way to receive an audio signal detected by the acoustic sensor while the vehicle is traveling, extract the characteristics of the audio signal and input said characteristics of the audio signal to the neural network in order to identify the type of road surface covered by the vehicle wheels.

Abstract: A generation system (100) of synthesized sound comprises: a first stage (1), wherein features (F) are extracted from an input raw sound and parameters of said features are evaluated; a second stage (2) wherein the evaluated parameters are used to create a plurality of physical models that are metrically evaluated in order to find the parameters of the best physical model, and a third stage (3) wherein the parameters of the best physical model are perturbed in order to create perturbed physical models and a metric evaluated of the perturbed physical models is performed to find the parameters of the best physical model.

Abstract: A computer-implemented method for detecting an anomalous operating status of a technical system. A training phase obtains a first set of time-series values generated by a digital twin simulation of the technical system for a regular operating status and a second set of time-series values measured by sensors in an anomalous operating status, and adjusts parameters of a machine learning model for detecting the regular operating status and for discriminating data samples of the regular operating status from data samples of the anomalous operating status to generate a trained machine learning model. A monitoring phase obtains a set of multivariate time-series values measured by the sensors, calculates an anomaly score value for determining whether the technical system is in an anomalous operating status based on the obtained set of multi-variate time-series values and the trained machine learning model, and outputs a signal including information on the determined anomalous operating status.

Abstract: A system for monitoring an acoustic scene outside a vehicle; the system including: a vehicle with wheels and a trunk, an acoustic sensor disposed in the trunk, a control unit operatively connected to the acoustic sensor, and at least one neural network operatively connected to the control unit, and trained in such a way to correlate the characteristics of an audio signal with types of road surfaces; the control unit is configured in such a way to receive an audio signal detected by the acoustic sensor while the vehicle is traveling, extract the characteristics of the audio signal and input said characteristics of the audio signal to the neural network in order to identify the type of road surface covered by the vehicle wheels.