Embodiments of the present disclosure disclose an earphone including a fixing structure, a first microphone array, a processor, and a speaker. The fixing structure is configured to fix the earphone near a user's ear without blocking the user's ear canal and including a hook-shaped component and a body part. The first microphone array is located in the body part and is configured to pick up environmental noise. The processor is located in the hook-shaped component or the body part and is configured to estimate a sound field at a target spatial position using the first microphone array and generate a noise reduction signal based on the estimated sound field. The target spatial position is closer to the user's ear canal than any microphone in the first microphone array. The speaker is located in the body part and is configured to output a target signal according to the noise reduction signal.

A system and method for reducing noise in a headphone compares the ambient sounds in a particular location to a plurality of noise reduction settings that are stored in a storage medium. The best noise reduction settings are then used to reduce noise.

The present disclosure relates to a method and system for generating personalized audio space in vehicle. Information related to user in each region of the vehicle is collected and analyzed to determine direction of first directional speakers associated with corresponding region. An audio space boundary for each region is identified based on the direction of first directional speakers in the corresponding region. Further, the proposed method renders first sound wave of a user selected audio using first directional speakers in the region and transmits a second sound wave corresponding to first sound wave in the region using second directional speakers associated with the corresponding region. The second sound wave restricts rendering of the first sound wave beyond the audio space boundary of the one of the one or more regions to generate the personalized audio space in the vehicle.

A method for feedforward noise cancellation in an enclosed space within a structure is provided. The method comprises placing a microphone array inside an inner surface of the enclosed space and conducting modal testing on an outside surface of the enclosed space, wherein the modal testing comprises multiple incoherent noise sources corresponding to locations of microphones in the microphone array. Noise generated by the modal testing is processed to create a number of acoustic mathematical models of the enclosed space. In response to incoherent noise within the enclosed space, a noise canceling signal is generated according to an output of the mathematical models.

An ear interface mode of headphones may be determined by measuring an acoustic response of the headphones. For example, the headphones may be determined to be in a leaky or sealed configuration. An adaptive noise cancellation (ANC) system may be controlled based on the determined ear interface mode of the headphones. For example, a set of configuration parameters may be loaded for the ANC system corresponding to the known ear interface mode. An anti-noise signal may be generated according to the selected configuration parameters, and that anti-noise signal added during playback of media, such as voice recordings, music, videos, or telephone call speech.

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.

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 car 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.

One embodiment provides a method, including: detecting, using one or more audio capture devices, nuisance audio; receiving, from one or more device sensors, contextual information; determining a mitigating audio signal based on the nuisance audio and contextual information; thereafter, emitting, from one or more audio source devices, mitigating audio into an interior space. Other aspects are described and claimed.

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.

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.