Abstract: Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.

Abstract: Embodiments herein relate to devices and related systems and methods for detecting falls. In an embodiment, a hearing assistance device is included having a first control circuit and a first motion sensor. The first motion sensor can be disposed in a fixed position relative to a head of a subject wearing the hearing assistance device. A first microphone and a first transducer for generating sound can be in operational communication with the first control circuit. The first control circuit can be configured to evaluate data from one or more sensors to detect a possible fall of a subject in physical contact with the hearing assistance device. The device can be configured to wirelessly transmit data regarding a possible fall to another device including an indication of whether the possible fall was detected binaurally or monoaurally. Other embodiments are included herein.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: An open-ear device performs active noise cancellation (ANC) for a user. A sensor located outside an ear of a user which does not occlude an ear canal of the ear and measures vibrational data indicative of a sound pressure level at a location outside the ear, or a level of pinna vibration of the user. A prediction pipeline generates a prediction of sound pressure within the ear canal using an individualized model, taking into account the measured vibrational data and the unique geometric shape of the user's head and pinna. This sound pressure prediction is used to generate audio instructions for rendering playback at an noise cancellation source, such as a bone conduction transducer and/or cartilage transducer, to perform ANC for the user by cancelling at least portion of the sound received at the ear canal.

Abstract: An artificial reality headset enhances audio signals from a target sound source using information from other devices in the local area. A primary headset broadcasts a location of a target sound source to secondary headsets in a local area. The secondary headsets transmit audio signals to the primary headset to enhance the audio content presented by the primary headset to a user. The secondary headset may select an array transfer function for the location of the target sound source. The secondary headsets correlate known transfer functions in the target direction with estimated transfer functions. The secondary headset may perform beamforming on the target sound source and transmit the output audio signal to the primary headset. In some embodiments, the secondary headset may transmit the array transfer function and a raw audio signal to the primary headset. The primary headset generates audio content based on the received audio signal.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.

Abstract: A method, comprising receiving at least one sound at an electronic device. The at least one sound is enhanced for the at least one user based on a compound metric. The compound metric is calculated using at least two sound metrics selected from an engineering metric, a perceptual metric, and a physiological metric. The engineering metric comprises a difference between an output signal and a desired signal. At least one of the perceptual metric and the physiological metric is based at least in part on input sensed from the at least one user in response to the received at least one sound.

Abstract: Various embodiments of a predictive fall event management system and a method of using such system are disclosed. The system includes a body-worn device and a controller operatively connected to the body-worn device. The controller is adapted to receive physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period; receive contextual data representative of context information of the wearer over the monitoring time period; and determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data. The controller is further adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.

Abstract: A method, comprising receiving at least one sound at an electronic device. The at least one sound is enhanced for the at least one user based on a compound metric. The compound metric is calculated using at least two sound metrics selected from an engineering metric, a perceptual metric, and a physiological metric. The engineering metric comprises a difference between an output signal and a desired signal. At least one of the perceptual metric and the physiological metric is based at least in part on input sensed from the at least one user in response to the received at least one sound.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.

Abstract: Various embodiments of a predictive fall event management system and a method of using such system are disclosed. The system includes a body-worn device and a controller operatively connected to the body-worn device. The controller is adapted to receive physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period; receive contextual data representative of context information of the wearer over the monitoring time period; and determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data. The controller is further adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.

Abstract: An audio system presented herein includes a transducer array, a sensor array, and a controller. The transducer array presents audio content to a user. The controller controls the transducer array to adjust a level of tactile content imparted to the user via actuation of at least one transducer in the transducer array while presenting the audio content to the user. The audio system can be part of a headset.

Abstract: Embodiments herein relate to systems, devices and methods for managing pharmacological therapeutics and aspects of the same. In an embodiment, a hearing assistance device can include a control circuit, an electroacoustic transducer for generating sound in electrical communication with the control circuit, a power supply circuit in electrical communication with the control circuit, and a sensor package in electrical communication with the control circuit. The control circuit can be configured to evaluate a signal from at least one of the sensors of the sensor package to detect administration of a therapy or receive data indicating that administration of a therapy has taken place. The control circuit can also be configured to record an instance of a detected medication administration event along with a timestamp. Other embodiments are also included herein.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: Embodiments herein relate to ear-worn devices and, more specifically, ear-worn devices that can measure reaction and/or reflex speeds. An ear-worn device herein can include a control circuit, a clock circuit in electrical communication with the control circuit, a motion sensor in electrical communication with the control circuit, an electroacoustic transducer for generating sound in electrical communication with the control circuit, and a power supply circuit in electrical communication with the control circuit. The ear-worn device can be configured to initiate generation of a stimulus sufficient to generate a response from the ear-worn device wearer. The ear-worn device can be configured to monitor for a qualified response to the stimulus and measure an amount of time between the stimulus and the qualified response. Other embodiments are also included herein.

Abstract: A method, comprising receiving at least one sound at an electronic device. The at least one sound is enhanced for the at least one user based on a compound metric. The compound metric is calculated using at least two sound metrics selected from an engineering metric, a perceptual metric, and a physiological metric. The engineering metric comprises a difference between an output signal and a desired signal. At least one of the perceptual metric and the physiological metric is based at least in part on input sensed from the at least one user in response to the received at least one sound.

Abstract: Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.

Abstract: Various embodiments of a monitoring system are disclosed. The monitoring system includes first and second sensors each adapted to detect a characteristic of a subject of the system and generate data representative of the characteristic of the subject, and a controller operatively connected to the first and second sensors. The controller is adapted to receive data representative of first and second characteristics of the subject from the first and second sensors, and determine statistics for first and second condition substates of the subject over a monitoring time period based upon the data received from the first and second sensors. The controller is further adapted to compare the statistics of the first and second condition substates, confirm the first condition substate if it is substantially similar to the second condition substate, and determine the statistics of an overall condition state of the subject based upon the confirmed first condition substate.