A control signal filter 2 receives a sound source signal determined by a control frequency specified in conformity with the vibration/noise source that produces vibration/noise, and outputs a control signal. A filter coefficient update unit 4 updates coefficients of the control signal filter 2 in response to a sound source signal and an error signal. A signal-to-interference ratio measuring unit 5 outputs a signal-to-interference ratio determined from the vibration/noise and the interference contained in the error signal in response to the control frequency and error signal. An update controller 6 adjusts an update step of the filter coefficient update unit 4 in accordance with the signal-to-interference ratio.

An active noise reduction (ANR) circuit includes a digital feed-forward ANR pathway coupled to a feed-forward microphone, to detect environmental sounds in an environment external to a casing, and to a first acoustic driver to output sounds within the casing. The digital feed-forward ANR pathway applies a plurality of filters using a first set of coefficients to convert signals from the feed-forward microphone to feed-forward anti-noise sounds to reduce environmental sounds within the casing. In response to a stimulus, the digital feed-forward ANR pathway applies the plurality of filters using a second set of coefficients, which reduce the degree of feed-forward ANR to enable human speech sounds in the environment external to the casing to be conveyed from the feed-forward microphone to the acoustic driver with less reduction than provided by the first plurality of filters.

An apparatus includes a noise reduction headphone comprising one or more microphones and an acoustic transducer, the one or more microphones configured to generate an input signal; and a controller comprising one or more processing devices, the controller configured to: process the input signal through one or more noise reduction filters to generate a noise-reduction signal, compare the input signal to an estimate of ambient noise to determine if the energy of the input signal is greater than the estimate of ambient noise, wherein if the energy of the input signal is greater than the estimate of ambient noise by a predetermined amount, a change in the noise reduction signal is suppressed; and generate an output signal, the output signal comprising, at least in part, the noise-reduction signal, wherein the acoustic transducer is configured to produce an acoustic output in accordance with the output signal.

The technology described in this document can be embodied in a method that includes receiving an input signal representing audio captured by a microphone of an active noise reduction (ANR) headphone, processing, by one or more processing devices, a portion of the input signal to determine a noise level in the input signal, and determining that the noise level satisfies a threshold condition. The method also includes, in response to determining that the noise level satisfies the threshold condition, generating an output signal in which ANR processing on the input signal is controlled in accordance with a target loudness level of the output signal, and driving an acoustic transducer of the ANR headphone using the output signal.

Embodiments of a diagnostic tool for an active noise cancelling system are presented. The diagnostic tool may generate an output from the active noise cancelling system and capture input to the active noise cancelling system that results from the output of the active noise cancelling system. The diagnostic tool may generate visual output to aid in evaluation of active noise cancelling systems.

Active noise control systems, devices, and methods are disclosed herein. Anti-snoring systems can include a first pillow unit having at least one error microphone and at least one speaker, at least one reference microphone configured to capture sound produced proximate to the at least one reference microphone, and a control unit operatively coupled to the first pillow unit and the at least one reference microphone. In some aspects, the control unit can be configured to produce an anti-noise in the at least one speaker disposed in the first pillow unit by processing signals received from the at least one error microphone and the at least one reference microphone using gated dynamic adjustments such that the anti-noise cancels any sound produced proximate the at least one reference.

A method for cancelling ambient audio sounds in the proximity of a transducer may include receiving an error microphone signal indicative of the output of the transducer and ambient audio sounds at the transducer. The method may also include generating an anti-noise signal for countering the effects of ambient audio sounds at an acoustic output of the transducer, wherein generating the anti-noise signal comprises applying a feedback filter having a response that generates a feedback anti-noise signal based on the error microphone signal and applying a variable gain element in series with the feedback filter. The method may further include monitoring whether an ambient audio event is occurring that could cause the feedback filter to generate an undesirable component in the anti-noise signal and controlling the gain of the variable gain element to reduce the undesirable component.

A headphone detector including a headphone and a processor. The headphone has a microphone and a speaker, and the microphone is configured to generate an audio signal based on an output of the speaker. The processor is configured to receive the audio signal, determine a characteristic of the audio signal, and assess whether the headphone is on ear or off ear based on a comparison of the characteristic to a threshold. In another aspect, an off-ear detection (OED) system includes a headphone and an OED processor. The headphone has a speaker, a feedforward microphone, and a feedback microphone. The OED processor is configured to determine whether the headphone is off ear or on ear, based at least in part on a headphone audio signal, a feedforward microphone signal, and a feedback microphone signal.

Systems and methods are provided an audio signal enhancement system that attenuates platform fan noise. Fan noise is a common type of self-noise in laptops and other devices, and fan noise can significantly degrade the quality of audio captured by built-in microphones. A neural network model is provided that enhances microphone Signal-to-Noise Ratio (SNR) and Signal-to-Distortion-plus-Noise Ratio (SDNR). The systems and methods also reduce algorithmic latency. The model architecture includes a Recurrent Neural Network, and a custom Gated Recurrent Unit layer is provided that uses fewer unique matrix weights and fewer biases and has fewer compute operations using fewer parameters. A platform self-noise suppression system is provided that eliminates low-amplitude platform self-noise signals. The model can predict when the platform fan is active, and remove the platform noise. In some examples, when the model predicts that the platform fan is not active, the model focuses on removing microphone self-noise.

A method performed by an audio output device is provided for detecting instabilities and taking mitigating actions. Specifically, an A-weighted dBA level of a raw feedback signal exceeding a threshold level trigger, at least, muting the driver. The described methods apply to detecting instabilities across a broad frequency spectrum.