A sound processing device includes a processor configured to generate a first frequency spectrum of a first sound signal corresponding to a first sound received at a first input device and a second frequency spectrum of a second sound signal corresponding to the first sound received at a second input device, calculate a transfer characteristic based on a first difference between an intensity of the first frequency spectrum and an intensity of the second frequency spectrum, generate a third frequency spectrum of a third sound signal transmitted from the first input device and a fourth frequency spectrum of a fourth sound signal transmitted from the second input device, specify a suppression level of an intensity of the fourth frequency spectrum based on a second difference between an intensity of the third frequency spectrum and an intensity of the fourth frequency spectrum.

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.

A method of calibrating an earphone may include: securing an ANC earphone to a calibration fixture, the calibration fixture including an ear model configured to support the ANC earphone, the ear model having an ear canal configured to anatomically resemble a human ear canal and a concha configured to anatomically resemble a human ear concha, the ear canal extending from the concha to an inner end of the ear canal; generating, with the ANC earphone, an audio signal based on a reference tone; determining a characteristic of the audio signal; comparing the characteristic of the audio signal to a previously determined reference characteristic; and adjusting a gain value of the ANC earphone based on the comparing. Additional methods and apparatus are also disclosed.

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.

A method of calibrating an earphone may include: securing an ANC earphone to a calibration fixture, the calibration fixture including an ear model configured to support the ANC earphone, the ear model having an ear canal configured to anatomically resemble a human ear canal and a concha configured to anatomically resemble a human ear concha, the ear canal extending from the concha to an inner end of the ear canal; generating, with the ANC earphone, an audio signal based on a reference tone; determining a characteristic of the audio signal; comparing the characteristic of the audio signal to a previously determined reference characteristic; and adjusting a gain value of the ANC earphone based on the comparing. Additional methods and apparatus are also disclosed.

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 reduction system includes a controller configured to acquire a plurality of noise signals output from a plurality of noise microphones, select a plurality of reference signals and an error signal from among the plurality of noise signals, the plurality of reference signals corresponding to a noise, the error signal corresponding to an error between the noise and a canceling sound, generate a control signal from the plurality of reference signals by using a plurality of control filters, and adaptively update the plurality of control filters by using a plurality of acoustic transmission filters. The plurality of acoustic transmission filters includes at least one adaptive update filter configured to be adaptively updated, and at least one non-adaptive update filter configured to be updated based on an update value of the adaptive update filter.

An audio device is disclosed, comprising multiple microphones and an audio module. The multiple microphones generate multiple audio signals. The audio module coupled to the multiple microphones comprises a processor, a storage media and a post-processing circuit. The storage media includes instructions operable to be executed by the processor to perform operations comprising: producing multiple instantaneous relative transfer functions (IRTFs) using a known adaptive algorithm according to multiple spectral representations for multiple first sample values in current frames of the multiple audio signals; and, performing distractor suppression over the multiple spectral representations and the multiple IRTFs using an end-to-end neural network to generate a compensation mask. The post-processing circuit generates an audio output signal according to the compensation mask. Each IRTF represents a difference in sound propagation between each predefined microphone and a reference microphone of the microphones relative to sound sources.

A processing circuit may include: (i) an adaptive filter having a response that generates an anti-noise signal from a reference microphone signal, wherein the response is shaped in conformity with the reference microphone signal and a playback corrected error, and wherein the playback corrected error is based on a difference between an error microphone signal and a secondary path estimate; (ii) a secondary path estimate filter configured to model an electro-acoustic path of a source audio signal and having a response that generates a secondary path estimate from the source audio signal; (iii) a secondary coefficient control block that shapes the response of the secondary path estimate filter in conformity with the source audio signal and the playback corrected error by adapting the response of the secondary path estimate filter to minimize the playback corrected error; and (iv) a noise injection portion for injecting a noise signal into the source audio signal, wherein the noise signal is shaped based on the playback corrected error.

An active noise cancellation device for cancelling a primary acoustic path between a noise source and a microphone by an overlying secondary acoustic path between a canceling loudspeaker and the microphone, the device comprising: a first input for receiving a microphone signal from the microphone; wherein the first electrical compensation path and the second electrical compensation path are coupled in parallel between a first node and the first input to provide the first noise canceling signal for a feed-backward prediction of the noise source; wherein the third electrical compensation path and the fourth electrical compensation path are coupled in parallel between a second node and the first input to provide the second noise canceling signal for a feed-forward prediction of noise source.