Starkey Laboratories

An effective howling prevention algorithm may provide improved sound quality of active noise cancellation (ANC) in audio amplification systems, such as in hearing assistance devices. A hearing device includes an inward-facing microphone and receiver, with a controller configured to monitor coherence between microphone and feedback signals to detect potential howling. When howling is detected based on an indicator of instability exceeding a threshold, the controller rapidly converges the gain to zero to prevent feedback howling. After a predetermined delay period without detected howling, the controller slowly converges the gain back to unity to restore active noise cancellation. This approach provides advantages over directly equating coherence to gain by more effectively preventing howling while maintaining optimal noise cancellation performance when stable.

This disclosure describes a hearing instrument comprising: a housing, a light source, a receiver, and one or more processors configured to: receive a device-finding signal from an external device, wherein the device-finding signal instructs the hearing instrument to enter a device-finding mode to assist a user in finding the hearing instrument; and in response to receiving the device-finding signal, cause the light source to emit a light signal; determine whether the hearing instrument is currently being worn; cause the receiver to output a first sound based on the hearing instrument currently being worn; and cause the receiver to output a second sound based on the hearing instrument not currently being worn.

Self-learning hearing device may enhance audio input based on an adaptive model. The adaptive, or changeable, model may be updatable in response to, among other things, user settings adjustments. For example, when a user makes a settings adjustment, the context surrounding, or corresponding to, the settings adjustment may be used to update the adaptive model.

A method comprises causing, by one or more processors of a hearing instrument, a receiver of the hearing instrument to output a sound; and causing, by the one or more processors, a light source of the hearing instrument to emit a first light signal indicating that the receiver output the sound.

An antenna for a hearing instrument comprises a first antenna segment; and a second antenna segment, wherein the first antenna segment is between a feed point of the antenna and the second antenna segment, the first antenna segment provides an in-line filter that is configured to attenuate one or more harmonic frequencies from an electrical current passing between the feed point and the second antenna segment via the first antenna segment.

Embodiments herein relate to ear-worn devices magnetic rechargeable interfaces. In an embodiment, an ear-worn device system for listening includes having a first ear-worn device having a first ear-worn device charging structure having an ear-worn device alignment structure with a magnetic material; and a charger case having a first case charging structure and a case alignment structure, wherein the first case charging structure includes a first case electrical contact, wherein the case alignment structure includes a first pole-aligned magnet and a second pole-aligned magnet having opposite poles and positioned on a first side of the case alignment structure, wherein the magnetic material is magnetically attracted to the first pole-aligned magnet and the second pole-aligned magnet. Other embodiments are also included herein.

Embodiments are directed to measuring, on a periodic basis by a hearing device, one or more transfer functions, each of the one or more transfer functions comprising a receiver-to-microphone acoustic transfer function of the hearing device. A plurality of health indicators of the hearing device are calculated over time using a measured characteristic of each of the measured transfer functions and a nominal characteristic of each of the measured transfer functions. Using the plurality of health indicators, degradation or failure of any of the receivers, any of the microphones, or a component in an acoustic path between one or more receivers and one or more microphones is detected. One or both of recommended actions and warnings are generated in response to detecting degradation or failure of any of the receivers, any of the microphones, or the component.

Various embodiments of an ear-wearable electronic device (10) that includes an acoustic boot (18) are disclosed. The device includes an enclosure (12) including a first housing (14), a second housing (16), and the acoustic boot (18) disposed between the first housing (14) and the second housing (16). The acoustic boot (18) includes a body having a first major surface, a second major surface, and an outer body surface (26) that extends between the first and second major surfaces and connects the first and second major surfaces. The acoustic boot (18) further includes a microphone coupler connected to the body and disposed within the enclosure (12), and an acoustic path (30) defined by the body, where the acoustic path extends between a first opening (32) defined by the outer body surface (26) and a second opening disposed within the microphone coupler and defined by a cavity of the microphone coupler.

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.

An ear-wearable device includes an acoustic sensor that receives ambient sound and produces an input signal. The device includes an acoustic transducer that reproduces sound in an ear of a user based on an output signal. A machine learning processing path includes: an encoder layer that encodes the input signal into a latent representation; a sound enhancement layer that produces an enhanced latent representation that provides an audio enhancement independent of an individual hearing pathology; a tuning layer that is configured to represent a user-specific hearing pathology and that modifies the enhanced latent representation to provide a tuned and enhanced latent representation that tailors the audio enhancement to compensate for the user-specific hearing pathology; and a decoder layer that decodes the tuned and enhanced latent representation to produce the output signal.