An active noise cancellation system (1) for cancelling environment noise perceived by a driver or passenger seated in a seat (3) mounted in a cabin of a vehicle, in combination with said seat, the seat comprising a seat cushion (19), a seat back (21) coupled to the seat cushion at a bottom end and extending upwards to a seat shoulder (23), and a headrest (22) coupled to the seat back, extending upwardly from the seat shoulder, the active noise cancellation system comprising an active noise cancellation circuit (ANC) (30), a plurality of microphones (10) mounted in the headrest and connected electrically to the ANC, and a plurality of speakers (16) mounted in the seat and connected electrically to the ANC circuit. The plurality of microphones comprises at least one first microphone mounted on a right side of the headrest and at least one second microphone mounted on a left side of the headrest, and the plurality of speakers comprises at least one first speaker mounted in the seat shoulder on a left side and at least one second speaker mounted in the seat shoulder on a right side, the right speaker configured to generate a noise cancellation sound from a noise signal picked up by the right microphone processed by the ANC circuit and the left speaker configured to generate a noise cancellation sound from a noise signal picked up by the left microphone processed by the ANC circuit.

An electronic device includes a speaker, a sensor, a communication circuit, a processor, and a memory to store instructions. The instructions, when executed by the processor, cause a wireless audio device to, while outputting a signal for reducing an external sound through the speaker, identify, using the communication circuit, an external electronic device, identify, using the sensor, a conversation responsive to a location of the external electronic device satisfying a specified condition, responsive to identifying the conversation, stop an output of the signal for reducing the external sound for a first period of time, and responsive to identifying a specified keyword included in the conversation, prolong stopping the output of the signal for reducing the external sound for a second period of time.

A fan control system applied to N fans inside a computer system is disclosed, comprising: a main microphone, a control circuitry, a wave generation circuitry and a number N of fan controllers. The control circuitry calculates a basic frequency value according to a temperature inside the computer system, and continuously updates a parameter by any known optimization algorithm according to a main audio signal from the main microphone. The wave generation circuitry generates N square waves according to the basic frequency value and the parameter. The N fan controllers respectively form and transmit N modulation signals to the N fans according to the N square waves and N tachometric signals from the N fans. The parameter is one of a frequency variation and a set of phase differences, and the N square waves have the same frequency.

Various implementations include systems for processing audio signals to remove artifacts introduced by a machine learning system in challenging environments. In particular implementations, a method includes generating a processed audio signal for a hearing assistance device in which the processed audio signal is intended to perceptually dominate a user auditory experience, including: processing an unprocessed audio signal received by the hearing assistance device, wherein the processing includes utilizing a machine learning (ML) system to generate an ML enhanced audio signal; determining a mixing coefficient from an environmental noise assessment; mixing the ML enhanced audio signal with the unprocessed audio signal using the mixing coefficient to generate the processed audio signal; and outputting the processed audio signal.

A vehicle noise cancellation system and method, operable for: receiving local contextual information affecting a soundwave present in a vehicle; receiving remote contextual information affecting the soundwave present in the vehicle; receiving vehicle occupant information; processing the local contextual information, the remote contextual information, and the vehicle occupant information to generate an augmented anti-soundwave; and delivering the augmented anti-soundwave to an occupant of the vehicle to mitigate the soundwave present in the vehicle. Delivering the augmented anti-soundwave to the occupant of the vehicle to mitigate the soundwave present in the vehicle may include delivering the first augmented anti-soundwave to an occupant present in the first zone of the vehicle and delivering the second augmented anti-soundwave to an occupant present in the second zone of the vehicle using a plurality of speakers.

Provided is a signal processing device that performs noise canceling by combining a feedforward method and a feedback method. A signal processing device includes: a correlation calculation unit that calculates a correlation between a first sound pickup signal by a first microphone installed outside a predetermined region and a second sound pickup signal by a second microphone installed in the predetermined region; a determination unit that determines the correlation; and a control unit that performs control based on a result of the determination. The control unit controls execution of signal processing for generating a cancellation signal to be output within the predetermined region from the first sound pickup signal and the second sound pickup signal or output of the cancellation signal.

A method for reducing noise within a vehicle cabin comprising at least one error sensor and at least one sound transducer, the method comprising: the at least one error sensor measuring at least one first noise at a first location; selecting at least one sound zone from a plurality of sound zones within the cabin for reducing noise in said at least one sound zone, based on a presence of a driver and passenger(s) within the cabin; estimating at least one second noise that would have been measured at a second location within the selected at least one sound zone, based on a primary transfer function describing a primary acoustic path from the first location to the second location; the at least one sound transducer generating at least one secondary noise for reducing the at least one second noise that would have been measured at the second location.

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.

The present invention provides a device for actively cancelling a target sound wavefront in an open space, the device comprising a signal processing module comprising at least one processor operatively coupled with a datastore, the at least one processor configured to: receive a data comprising one or more geographical features, and one or more audio features generated by one or more receiving microphones having a geographical relationship with an array of receiving microphones in an area adjacent to a user; process aid data using a prediction model adapting a trained deep learning framework; and provide output the inverse sound wavefront of the target sound at the area of said predicting microphones.

An adaptive feedback canceller of an ear-wearable device has an adaptive foreground filter that inserts a feedback cancellation signal into a digitized input signal to produce an error signal. An instability detector of the device is configured to extract wo or more features from the error signal. The instability detector has a machine learning module that determines instability in the error signal based on the two or more features. The instability module changes the adaptive foreground filter in response to determining the instability. The change causes the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.