A computer-implemented method for generating anti-noise using an anti-noise generator to suppress noise from a noise source in an environment comprises processing a sound signal, which is representative of ambient sound including noise, anti-noise and propagation noise from the environment, using a deep learning algorithm configured to generate an anti-noise signal to form anti-noise. The deep learning algorithm comprises a convolution layer; after the convolution layer, a series of atrous scaled convolution modules, wherein each of the atrous scaled convolution modules comprises an atrous convolution, a nonlinear activation function after the atrous convolution, and a pointwise convolution after the nonlinear activation function; after the series of atrous scaled convolution modules, a recurrent neural network; and after the recurrent neural network, a plurality of fully connected layers.

The noise suppression method of individual active noise reduction system comprises the steps that: (1) initial noise digital signals are received and converted to serve as input signals of a BP neural network; (2) the input signals are processed to generate secondary digital signals; (3) the secondary digital signals are output to a loudspeaker and secondary noise is generated; (4) remained noise digital signals obtained by overlapping the initial noise and the secondary noise are received; whether remained noise digital signals is continuously constant for the set times is judged; if yes, the secondary digital signals are kept outputting; (5) if not, BP neural network parameters are optimized and adjusted with the amplitude of the remained noise digital signals being minimum as the optimality principle; remained noise digital signals of previous step are served as new input signals and the step (2) is executed again.

An active noise reduction system includes a reference signal generator configured to generate a reference signal, a canceling sound generator configured to generate a canceling sound, an error detector configured to detect an error between a noise and the canceling sound and generate an error signal corresponding to the error, and a controller configured to control the canceling sound generator based on the reference signal and the error signal, wherein the controller is configured to update an estimation value of acoustic characteristics in an internal space of a mobile body based on the reference signal and the error signal, estimate a head position of an occupant in the internal space based on the updated estimation value of the acoustic characteristics, and update a control filter based on the estimated head position of the occupant, the control filter being a filter for controlling the canceling sound generator.

A noise suppressing method, a noise suppressing device and a noise suppressing system are provided. The noise suppressing method includes: receiving internal noise acquired by a reference voice acquisition mechanism and a voice signal containing external noise acquired by a primary voice acquisition mechanism, when the voice signal is inputted; extracting an internal signal feature corresponding to the internal noise, where the internal signal feature is a power spectrum frame sequence; acquiring an external approximate feature corresponding to the external noise based on the internal signal feature and a pre-set mapping formula; converting the external approximate feature into a noise signal estimate by the inverse Fourier transform; and performing a pre-set noise cancellation process on the noise signal estimate and the acquired voice signal containing the internal noise, to obtain a noise-suppressed de-noised voice signal.

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 audio processing system, such as an active noise cancellation system, and method suppresses tonal howling in a feedback system based on a gain enhancement system that emphasizes the howling signal and deemphasizes non-howling signals. The howling signal is extracted from an error signal generated from sound from a speaker sensed by an error sensor. The gain enhancement signal is generated based on a first power ratio between a filtered reference signal, generated based on sound sensed from external noise by a reference sensor, and a filtered error signal and/or a second power ratio between two filtered error signals with different passbands. Using the gain enhancement signal and the howling signal, a howling suppression gain signal is generated and used to amplify the error signal. A feedback signal produced based on the amplified error signal is provided to the speaker as an anti-noise signal with suppressed howling.

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.

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.

For example, a controller of an Active Acoustic Control (AAC) system may be configured to process input information including AAC configuration information, and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations. For example, the controller may be configured to process the input information to determine a sound control pattern to control sound within a sound control zone based on the plurality of noise inputs. For example, the controller may include a Neural-Network (NN) trained to generate an NN output based on an NN input, wherein the NN input is based on the AAC configuration information. For example, the controller may be configured to generate the sound control pattern based on the NN output, and to output the sound control pattern to one or more acoustic transducers.

Embodiments of the present disclosure set forth a method or reducing noise in an audio environment. The method includes acquiring, via one or more sensors, a plurality of audio signals associated with sound in an audio environment; determining that a first audio signal in the plurality of audio signals matches a first reference signal in a set of reference signals; generating, based on the first audio signal, a first directional audio signal wherein, when the first directional audio signal is outputted by a loudspeaker, the loudspeaker produces a first acoustic field that attenuates the first audio signal at a position of a user; determining that a second audio signal in the plurality of audio signals does not match at least one reference signal in the set of reference signals; and storing data associated with the second audio signal as an additional reference signal in the set of reference signals.