EAR-WEARABLE DEVICE MODELING
    1.
    发明公开

    公开(公告)号:US20230351064A1

    公开(公告)日:2023-11-02

    申请号:US18304996

    申请日:2023-04-21

    CPC classification number: G06F30/00 A61F11/06

    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.

    HEARING DEVICE WITH MULTIPLE NEURAL NETWORKS FOR SOUND ENHANCEMENT

    公开(公告)号:US20230292074A1

    公开(公告)日:2023-09-14

    申请号:US17927239

    申请日:2021-05-18

    CPC classification number: H04S7/30 G06N3/08

    Abstract: An ear-wearable device stores a plurality of neural network data objects each defining a respective neural network. A sound signal received from a microphone of the ear-wearable device is digitized. An ambient environment of the digitized sound signal is classified into one of a plurality of classifications. Based on the classification, one of the neural network data objects is selected to enhance the digitized sound signal. An analog signal is formed based on the enhanced digitized sound signal. The analog signal is reproduced via a receiver of the ear-wearable device.

    Hearing device with multiple neural networks for sound enhancement

    公开(公告)号:US12302084B2

    公开(公告)日:2025-05-13

    申请号:US17927239

    申请日:2021-05-18

    Abstract: An ear-wearable device stores a plurality of neural network data objects each defining a respective neural network. A sound signal received from a microphone of the ear-wearable device is digitized. An ambient environment of the digitized sound signal is classified into one of a plurality of classifications. Based on the classification, one of the neural network data objects is selected to enhance the digitized sound signal. An analog signal is formed based on the enhanced digitized sound signal. The analog signal is reproduced via a receiver of the ear-wearable device.

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