Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: A mobile device sends to a radio communicatively coupled to the mobile device, an instruction to monitor a frequency band. It is determined that the frequency band is jammed by a jamming source. The mobile device receives, from the radio, signal waveform information corresponding to the frequency band that is received by the radio from a 360 span about the radio. Based on the waveform information a direction of the jamming source is determined. The mobile device presents on a display device information that indicates that the frequency band is being jammed, and a direction of the jamming source with respect to a current location of the radio.

Abstract: Embodiments herein relate to ear-worn devices that can be used to locate other devices such as mobile electronic devices. In a first aspect, an ear-worn device is included having a control circuit, a microphone in electrical communication with the control circuit, an electroacoustic transducer for generating sound in electrical communication with the control circuit, a motion sensor in electrical communication with the control circuit, and a power supply circuit in electrical communication with the control circuit, wherein the ear-worn device is configured to issue a location command to a mobile electronic device and wherein the location command triggers the mobile electronic device to emit a locating signal. Other embodiments are also included herein.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: Disclosed herein, among other things, are systems and methods for wireless communication with hearing devices. A method includes configuring, using a first hearing device configured to be worn or implanted in a first ear of a user, a first device parameter data. The method further includes transmitting, from the first hearing device via a wireless connection to a second hearing device configured to be worn or implanted in a second ear of the user, the first device parameter data. The method also includes receiving, at the second hearing device via the wireless connection, the first device parameter data from the first hearing device, and configuring the second hearing device for coordinated binaural operations with the first hearing device using the first device parameter data.

Abstract: Embodiments herein relate to hearing assistance systems and devices. In an embodiment, a hearing assistance device is included having a control circuit, a microphone, a motion sensor, a receiver, and a sound processor. The hearing assistance device is configured to output sound to a wearer of the hearing assistance device with the receiver based on sound detected with the microphone, detect chewing of the wearer by evaluating signals from the microphone and/or the motion sensor, and modulate a gain value of the sound processor for sounds output through the receiver based on the chewing detected. Other embodiments are also included herein.

Abstract: Embodiments herein relate to car-wearable devices and systems that can analyze a device wearer's gait and/or provide gait training. In a first aspect, an car-wearable device is included having a control circuit, a motion sensor in electrical communication with the control circuit, a microphone in electrical communication with the control circuit, and an electroacoustic transducer in electrical communication with the control circuit, wherein the car-wearable device is configured to calculate one or more desired gait parameters, and provide a series of audio cues to a device wearer consistent with the one or more desired gait parameters.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: Embodiments herein relate to ear-wearable devices that can be used to control other device and related methods. In a first aspect, an ear-wearable device is included having a control circuit, a wireless communications circuit, a microphone, and a motion sensor. The ear-wearable device is configured to receive an input from a wearer of the ear-wearable device, select a controllable device to be controlled based on factors including at least one of physical proximity between the ear-wearable device and the controllable device and a direction that the head of the wearer is pointing, and issue a control command to the controllable device. Other embodiments are also included herein.

Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Abstract: Embodiments herein relate to ear-wearable stress and anxiety monitoring systems, devices and methods. Embodiments herein further relate to ear-wearable systems and devices that can detect and take actions to alleviate device wearer's stress and anxiety. In an embodiment an ear-wearable stress and/or anxiety monitoring system is included having a control circuit, a microphone, and a sensor package that can include a motion sensor. The ear-wearable system is configured to evaluate data from at least one of the microphone and the sensor package and classify a stress level of a device wearer using a machine learning classification model and periodically update the machine learning classification model based on indicators of stress experienced by the device wearer.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Abstract: Embodiments herein relate to ear-wearable devices that can be used to control other device and related methods. In a first aspect, an ear-wearable device is included having a control circuit, a wireless communications circuit, a microphone, and a motion sensor. The ear-wearable device is configured to receive an input from a wearer of the ear-wearable device, select a controllable device to be controlled based on factors including at least one of physical proximity between the ear-wearable device and the controllable device and a direction that the head of the wearer is pointing, and issue a control command to the controllable device. Other embodiments are also included herein.

Abstract: Systems and methods that can utilize the detection of human occupancy without fiducial elements to control an environmental, security, or other system within a structure. The systems and method can initiate communication to a human user directly, and can alter their operation based on human presence.

Abstract: A hearing assisting system is included having a first microphone and a second microphone. The system further includes a vision device having a display device, where the display device is configured to display visual information to the user when the user is wearing the vision device. The system is further configured to process audio signals to identify first audio content information and first audio direction information related to the first audio content information. The system further can transmit, to the vision device, first display information to cause the vision device to display a non-transcript content representation of the first audio content information and a direction representation of the first audio direction information using the display device.

Abstract: Disclosed herein, among other things, are systems and methods for person-to-person voice communication between ear-wearable devices. A method includes receiving, using a microphone of a first hearing device configured to be worn on or in an ear of the first user, a first acoustic own voice signal from the first user. The method further includes transmitting, from the first hearing device via a wireless connection to a second hearing device configured to be worn on or in an ear of a second user, a first audio packet based on the received first acoustic own voice signal. The method also includes receiving, at the second hearing device via the wireless connection, the first audio packet from the first hearing device, and playing, at the second hearing device using a second receiver, an output signal for the second user based on the first audio packet.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.