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: A method comprises obtaining, by one or more processors of a computing system, ear modeling data representing an impression of an ear of a patient; for each of a plurality of combinations of one or more types of hearing instruments and one or more feature sets, determining, by the one or more processors, whether the combination is feasible for a shape of the ear of the patient, wherein different feature sets of the one or more feature sets include different combinations of components; and outputting, by the one or more processors, for display, a graphical user interface (GUI) that presents one or more of the combinations that are determined to be feasible given the shape of the ear of the patient.

Abstract: One or more processing circuits may obtain sensor data that includes first sensor data generated by a first plurality of sensors of one or more hearing instruments worn by a user and second sensor data generated by a second plurality of sensors of the computing device communicably coupled to the one or more hearing instruments. The one or more processing circuits may generate a notification and may determine, based on at least a portion of the sensor data, a. time period at which the user is available to interact with the notification. The one or more processing circuits may, in response to determining the time period at which the user is available to interact with the notification, output, during the time period, the notification.

Abstract: A method comprises obtaining, by one or more processors, a component model, the component model being a 3-dimensional (3D) model of a component of a hearing instrument; obtaining, by the one or more processors, a plurality of ear impression models, each respective ear impression model of the plurality of ear impression models being a 3D model of an ear canal of a user corresponding to the respective ear impression model; for each respective ear impression model, performing, by the one or more processors, a component optimization process that optimizes a position of the component model within the respective ear impression model in 6-degrees of freedom based on one or more optimization criteria; generating, by the one or more processors, statistical data based on the positions of the components model within the ear impression models; outputting, by the one or more processors, the statistical data.

Abstract: One or more processing circuits may obtain context information associated with a user of one or more hearing instruments, wherein the context information is based on a first set of sensor data generated by a plurality of sensors of the one or more hearing instruments and a second set of sensor data generated by a plurality of sensors of a computing device communicatively coupled to the one or more hearing instruments. The one or more processing circuits may determine, based on at least a portion of the context information, an auditory intent of the user for a given auditory context. The one or more processing circuits may associate the auditory intent with one or more actions, such as actions to adjust one or more settings of the one or more hearing instruments. The one or more processing circuits may invoke the one or more actions associated with the auditory intent.

Abstract: Disclosed herein, among other things, are systems and methods for humidity detection for hearing device applications. A method includes calculating a capacitance between a terminal of a processor within a housing of an ear-wearable device and a designated ground plane within the housing. A temperature measurement is obtained from a sensor within the housing, and a humidity level is determined within the housing using the calculated capacitance and the obtained temperature measurement. The determined humidity level is compared to one or more predetermined humidity trigger levels, and a notification is sent to a wearer of the ear-wearable device conditioned on the comparison.

Abstract: Embodiments herein relate to ear-wearable devices and systems that can be used to screen for orthostatic intolerance conditions. In an embodiment, an ear-wearable device can be included having a control circuit, a microphone, an electroacoustic transducer, and a sensor package. The sensor package can include a motion sensor and an optical sensor. The ear-wearable device can be configured to process signals from the motion sensor to detect a postural transition of a device wearer to a standing position, trigger operation of the optical sensor, and process signals from the optical sensor to screen for an orthostatic intolerance condition. Other embodiments are also included herein.

Abstract: A processing system may receive reference audio data representing one or more voices and may generate, using a first machine learning (ML) model, an embedding of the reference audio data. The processing system receives live audio data representing sound detected by one or more microphones of a hearing instrument and may generate an input spectrogram of the live audio data. The processing system may use a second ML model to generate a masked spectrogram based on the embedding and the input spectrogram. The masked spectrogram represents a version of the live audio data in which portions of the live audio data spoken in the voices represented by the reference audio data are enhanced. The processing system may cause one or more receivers of the hearing instrument to output sound based on the masked spectrogram.

Abstract: One or more processing circuits may obtain sensor data that includes first sensor data generated by a first plurality of sensors of one or more hearing instruments worn by a user and second sensor data generated by a second plurality of sensors of the computing device communicably coupled to the one or more hearing instruments. The one or more processing circuits may generate a notification and may determine, based on at least a portion of the sensor data, a. time period at which the user is available to interact with the notification. The one or more processing circuits may, in response to determining the time period at which the user is available to interact with the notification, output, during the time period, the notification.

Abstract: A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Abstract: One or more processing circuits may obtain context information associated with a user of one or more hearing instruments, wherein the context information is based on a first set of sensor data generated by a plurality of sensors of the one or more hearing instruments and a second set of sensor data generated by a plurality of sensors of a computing device communicatively coupled to the one or more hearing instruments. The one or more processing circuits may determine, based on at least a portion of the context information, an auditory intent of the user for a given auditory context. The one or more processing circuits may associate the auditory intent with one or more actions, such as actions to adjust one or more settings of the one or more hearing instruments. The one or more processing circuits may invoke the one or more actions associated with the auditory intent.

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: Disclosed herein, among other things, are systems and methods for humidity detection for hearing device applications. A method includes calculating a capacitance between a terminal of a processor within a housing of an ear-wearable device and a designated ground plane within the housing. A temperature measurement is obtained from a sensor within the housing, and a humidity level is determined within the housing using the calculated capacitance and the obtained temperature measurement. The determined humidity level is compared to one or more predetermined humidity trigger levels, and a notification is sent to a wearer of the ear-wearable device conditioned on the comparison.

Abstract: A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Abstract: Embodiments herein relate to ear-wearable systems and devices that can detect respiratory conditions and related parameters. In an embodiment, an ear-wearable device for respiratory monitoring is included having a control circuit, a microphone, and a sensor package. The ear-wearable device can be configured to analyze signals from the microphone and/or the sensor package and detect a respiratory condition and/or parameter based on analysis of the signals. In an embodiment, an ear-wearable system for respiratory monitoring is included having an accessory device and an ear-wearable device. In an embodiment, a method of detecting respiratory conditions and/or parameters with an ear-wearable device system is included. Other embodiments are also included herein.

Abstract: A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Abstract: An ear-wearable electronic device comprises a motion sensor configured to generate motion information and a first respiration rate estimated using the motion information. A PPG sensor is configured to generate PPG data and a second respiration rate estimate using the PPG data. A processor is configured to produce a respiration rate estimate using the first and second respiration rate estimates. A communication device is configured to communicate the respiration rate estimate to one or both of an external electronic device and a cloud database.

Abstract: A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.