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: 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: 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 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.

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.

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: An audio system processes a speech signal to maintain a target value of the speech intelligibility index (SII) while minimizing the overall speech level so that speech intelligibility is preserved across different environmental sound levels while possible distortions and overall loudness are mitigated. In one embodiment, a hearing aid processes a speech signal received from another device to maintain a target value of the SII while minimizing the overall speech level before mixing the speech signal with a microphone signal.

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: 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: An audio system processes a speech signal to maintain a target value of the speech intelligibility index (SII) while minimizing the overall speech level so that speech intelligibility is preserved across different environmental sound levels while possible distortions and overall loudness are mitigated. In one embodiment, a hearing aid processes a speech signal received from another device to maintain a target value of the SII while minimizing the overall speech level before mixing the speech signal with a microphone signal.

Abstract: Disclosed herein, among other things, are methods and apparatus for improving speech intelligibility for speech-in-noise in audio processing and hearing assistance devices. The present subject matter includes a method for improving speech intelligibility for speech-in-noise for audio processing and hearing assistance devices. The method includes receiving an audio signal using a microphone array and processing the received signal to improve speech intelligibility in noise. A barrier-type beamforming process is used to improve signal-to-noise ratio at the output of the microphone array. The beamforming process includes convex optimization using a logarithmic barrier function, according to various embodiments.

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.