In some embodiments, the electronic device includes a speaker, a microphone, a memory, a digital signal processor (DSP), a driver, and a processor. The processor is configured to: obtain a first sound signal by combining a first signal, a second signal, and a first anti-phase signal; extract, from a second sound signal related to the first sound signal, a first DPOAE signal; obtain a third sound signal by combining a fourth signal, a fifth signal, and a second anti-phase signal; extract, from a fourth sound signal related to the third sound signal, a second DPOAE signal; obtain a user hearing profile based on the first and second DPOAE signals; and perform, based on the user hearing profile, at least one of a sound volume change and an equalization (EQ) change of a sound to be output.

In some embodiments, a method for performing at least one of enhancement, decoding, or rendering of a multichannel audio signal in response to compression feedback or feedback from a smart amplifier. For example, the compression feedback may be indicative of amount of compression applied to each of multiple frequency bands, of the audio signal or an enhanced audio signal generated in response thereto. The enhancement (e.g., bass enhancement) may include dynamic routing of audio content of the input audio signal between channels of an enhanced audio signal generated in response thereto. The enhancement and compression may be performed on a per speaker class basis. Other aspects are systems (e.g., programmed processors) and devices (e.g., devices having physically-limited bass reproduction capabilities, such as, for example, a notebook or laptop computer, tablet, soundbar, mobile phone, or other device with small speakers) configured to perform any embodiment of the method.

A multi-channel microphone receiver (MCR) for two or more wireless microphones (M.sub.1, . . . , M.sub.N) comprises a network interface and at least one mixer (MX) adapted for mixing audio signals (D.sub.1, . . . , D.sub.N) of the microphones. The mixer may be configured without any reconfiguration of the actual network being required. In addition to the single audio channels, the audio signal mixed according to the configuration may be output via a separate audio output channel (DO.sub.Mx, AO.sub.Mx), which may be analog or digital.

Aspects of the present disclosure provide methods, apparatuses, and systems for closed-loop sleep protection and/or sleep regulation. According to an aspect, sleep disturbing noises are predicted and a biosignal parameter is measured to dynamically mask predicted disturbing environmental noises in the sleeping environment with active attenuation. Environmental noises in a sleeping environment of a subject are detected, input, or predicted based on historical data of the sleeping environment collected over a period of time. The biosignal parameter is used to determine sleep physiology of a subject. Based on the environmental noises in the sleeping environment and the determined sleep physiology, the noises are predicted to be disturbing or non-disturbing noises. For predicted disturbing noises, one or more actions are taken to regulate sleep and avoid sleep disruption by using sound masking prior to or concurrently with the occurrence of the predicted disturbing noises.

A portable calibration system is disclosed that calibrates an audio equipment without using a dedicated sound level meter. The calibration system comprises a coupler configured to couple a transducer to an energy sensor, where an output of the transducer is provided to the energy sensor via the coupler, an analyzer module configured to receive information from the energy sensor regarding the output of the transducer, a processor, in the analyzer module, configured to process the information to provide a result of a calibration for the audio equipment with respect to expected results, and a display configured to display the result of the calibration.

The subject technology receives, from a first sensor of a device, first sensor output of a first type. The subject technology receives, from a second sensor of the device, second sensor output of a second type, the first and second sensors being non-touch sensors. The subject technology provides the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted touch-based gesture based on sensor output of the first type and sensor output of the second type. The subject technology provides a predicted touch-based gesture based on output from the machine learning model. Further, the subject technology adjusts an audio output level of the device based on the predicted gesture, and where the device is an audio output device.

The disclosure relates to a hearing system comprising a first hearing device and a second hearing device configured to be worn at a respective ear of a user, each hearing device comprising a displacement sensor configured to provide respective displacement data indicative of a rotational displacement and/or a translational displacement of the hearing device, and a processing unit communicatively coupled to the displacement sensors. The disclosure further relates to a corresponding method of operating a hearing system, and a computer-readable medium storing instructions to perform the method.

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 signal representing a sound can be received. A machine learning model can be run to identify that the sound triggers a reaction in a user hearing the sound. A preventive action can be automatically activated to mitigate the reaction. The user's reactions can be monitored. Responsive to determining that the user's reaction has been mitigated or suppressed, the preventive action can be deactivated. The machine learning model can be retrained using at least the signal as new training data.

A Personal Communication Device (PCD) is provided and includes a microphone, a speaker, and a PCD body, wherein the PCD body is configured as an ear cuff and includes a body first end, a body second end and a body middle portion. The PCD body further includes sound processing electronics, such that when a sound is received by the microphone, the sound processing electronics process the sound to generate a processed sound and communicates the processed sound to the speaker and outputs the processed sound into the user's ear canal, wherein the PCD is configured to be securely associated with an ear of a user such that when the PCD is associated with the ear of the user, and wherein the PCD is configured for wireless connectivity.