Abstract: A method for adjusting a noise generated by a device. The method includes: ascertaining, for each device feature, a dependence of a contribution of the device feature to a noise evaluation characteristic number on the value of the device feature by training an EBM with combinations of values for the device features and, for each of the combinations, an associated value of the noise evaluation characteristic number, each of a plurality of decision trees of the EBM ascertaining a contribution to the noise evaluation characteristic number for a device that has the value for the device feature; ascertaining values of the device features for a device of which the noise is to be adjusted; ascertaining changes for one or more of the device features to the values of the device features for improving the noise evaluation characteristic number; and adjusting the device according to the ascertained changes.

Abstract: A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.

Abstract: A system includes a processor, wherein the processor is programmed to receive sound information and vibrational information from a device in a first environment, generate a training data set utilizing at least the vibrational information and a sound perception score associated with the corresponding sound of the vibrational information, wherein the training data set is fed into an un-trained machine learning model, in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model, receive real-time vibrational information from the device in a second environment, and based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound perception score indicating characteristics associated with sound emitted from the device.