Noises that are to be emitted by an aerial vehicle during operations may be predicted using one or more machine learning systems, algorithms or techniques. Anti-noises having equal or similar intensities and equal but out-of-phase frequencies may be identified and generated based on the predicted noises, thereby reducing or eliminating the net effect of the noises. The machine learning systems, algorithms or techniques used to predict such noises may be trained using emitted sound pressure levels observed during prior operations of aerial vehicles, as well as environmental conditions, operational characteristics of the aerial vehicles or locations of the aerial vehicles during such prior operations. Anti-noises may be identified and generated based on an overall sound profile of the aerial vehicle, or on individual sounds emitted by the aerial vehicle by discrete sources.

An active noise control method and system for a vehicle are provided. The active noise control method for a vehicle includes performing active noise control (ANC) to reduce noise introduced from the outside of the vehicle to the inside of the vehicle and received through a microphone, determining whether a level of residual noise of the noise reduced by the ANC is greater than a threshold value, and performing secondary path model re-measurement when an engine of the vehicle is turned off if the level of the residual noise is greater than the threshold value.

An active noise control method and system for a vehicle are provided. The active noise control method for a vehicle includes performing active noise control (ANC) to reduce noise introduced from the outside of the vehicle to the inside of the vehicle and received through a microphone, determining whether a level of residual noise of the noise reduced by the ANC is greater than a threshold value, and performing secondary path model re-measurement when an engine of the vehicle is turned off if the level of the residual noise is greater than the threshold value.

Certain aspects provide methods and apparatus for recovering audio quality of voice when processing signals associated with a wearable audio output device. A method that may be performed includes receiving, by an in-ear microphone acoustically coupled to an environment inside an ear canal of a user, an audio signal having a first frequency band, predicting high-frequency band information for the audio signal using a model trained using training data of known high-frequency bands associated with low-frequency bands, generating an output signal having a second frequency band based, at least in part, on the first frequency band of the audio signal and the predicted high-frequency band information for the audio signal, and outputting, by the wearable audio output device, the output signal having the second frequency band.