TRANSFER FUNCTION GENERATION SYSTEM AND METHOD

    公开(公告)号:US20240221714A1

    公开(公告)日:2024-07-04

    申请号:US18570152

    申请日:2022-06-17

    Abstract: A system for generating a transfer function indicating audio transmission characteristics of an input device associated with a processing device includes: a parameter determining unit configured to determine one or more parameters of the input device, where the parameters include a location of a microphone associated with the input device and a location of one or more respective input and/or output elements associated with the input device, an audio characteristic determining unit configured to determine one or more characteristics of audio transmission between respective input and/or output elements and the microphone, where the characteristics include one or more of an attenuation, resonance, and/or change in frequency profile of audio associated with the respective input and/or output element, and a transfer function generating unit configured to generate a transfer function in dependence upon the determined audio characteristics.

    AUDIO PERSONALISATION METHOD AND SYSTEM
    2.
    发明公开

    公开(公告)号:US20230413005A1

    公开(公告)日:2023-12-21

    申请号:US18246938

    申请日:2021-09-15

    CPC classification number: H04S7/303 H04S2420/01

    Abstract: An audio personalisation method for a first user includes: testing a first user on a calibration test, the calibration test comprising requiring a user to match a test sound to a test location, either by controlling the position of the presented sound or controlling the position of the presented location, for a sequence of test matches, each test sound being presented at a position using a default head related transfer function ‘HRTF’, receiving an estimate of each matching location from the first user, and calculating a respective error for each estimate, to generate a sequence of location estimate errors for the first user; and comparing at least some of the location estimate errors for the first user with estimate errors of the same locations previously generated for at least a subset of a corpus of reference individuals; identifying a reference individual with the closest match of compared location estimation errors to those of the first user; and using an HRTF, previously obtained for the identified reference individual, for the first user.

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