Abstract:
Embodiments herein relate to ear-worn devices and related systems and methods that can be used detect oropharyngeal events and related occurrences such as food and drink intake. In an embodiment, an ear-worn device system is included having a first ear-worn device that has a control circuit, a motion sensor, at least one microphone, an electroacoustic transducer, and a power supply circuit. The system can also include a second ear-worn device. The system can be configured to monitor signals from at least one of the motion sensor and the at least one microphone and evaluate the signals to identify oropharyngeal events and/or related occurrences. Other embodiments are also included herein.
Abstract:
A hearing assistance system obtains a first input audio signal that is based on sound received by a first set of microphones. The system also obtains a second input audio signal that is based on sound received by a second, different set of microphones. A first adaptive beamformer generates a first output audio signal based on the first input audio signal, the second input audio signal, and a value of a first parameter. A second adaptive beamformer generates a second output audio signal based on the first input audio signal, the second input audio signal, and a value of a second parameter. The value of the first parameter and the value of the second parameter are determined such that a magnitude squared coherence (MSC) of the first output audio signal and the second output audio signal is less than or equal to the coherence threshold.
Abstract:
Disclosed herein, among other things, are apparatus and methods for annoyance perception and modeling for hearing-impaired listeners. One aspect of the present subject matter includes a method for improving noise cancellation for a wearer of a hearing assistance device having an adaptive filter. In various embodiments, the method includes calculating an annoyance measure or other perceptual measure based on a residual signal in an ear of the wearer, the wearer's hearing loss, and the wearer's preference. A spectral weighting function is estimated based on a ratio of the annoyance measure or other perceptual measure and spectral energy. The spectral weighting function is incorporated into a cost function for an update of the adaptive filter. The method includes minimizing the annoyance or other perceptual measure based cost function to achieve perceptually motivated adaptive noise cancellation, in various embodiments.
Abstract:
A method comprises obtaining ear modeling data, wherein the ear modeling data includes a 3D model of an ear canal; applying a shell generation to generate a shell shape based on the ear modeling data, wherein the shell-generation model is a machine learning model and the shell shape is a 3D representation of a shell of an ear-wearable device; applying a set of one or more component-placement models to determine, based on the ear modeling data, a position and orientation of a component of the ear-wearable device, wherein the component-placement models are independent of the shell-generation model and each of the component-placement models is a separate machine learning model; and generating an ear-wearable device model based on the shell shape and the 3D arrangement of the components of the ear-wearable device.
Abstract:
An ear-wearable device includes a receiver that produces sound into an ear canal and an inward-facing microphone determining sound pressure resulting from: the sound reproduced by the receiver into the ear canal; and acoustical noise leaking into the ear canal. A structural vibration sensor is coupled to detect at least one of body-induced vibrations and receiver-induced vibrations and produce a sensed vibration signal in response. A sound processor of the ear-wearable device is operable to determine an error signal from the inward-facing microphone and determine an active noise cancellation (ANC) signal based on the error signal. The vibration signal is used to reduce the impacts of vibrations on ANC processing within the ear-wearable device.
Abstract:
Disclosed is a multi-mode beam former, comprising a device for receiving a multi-mode input signal, and a device for constructing an optimization model and solving the optimization model to obtain a beam-forming weight coefficient for performing linear or non-linear combination on the multi-mode input signal. The optimization model comprises an optimization formula for obtaining the beam-forming weight coefficient. The optimization formula comprises: establishing an association between at least one electroencephalogram signal and a beam forming output, and optimizing the association to construct the beam-forming weight coefficient associated with the at least one electroencephalogram signal.
Abstract:
Disclosed is a beam former, comprising: an apparatus for receiving a plurality of input signals; an apparatus for optimizing a mathematical model and solving an algorithm, which obtains a beam forming weight coefficient for carrying out linear combination on the plurality of input signals; and an apparatus for generating an output signal to the beam forming weight coefficient and the plurality of input signals.