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    NAVIGATION BY SPATIAL PLACEMENT OF SOUND
    2.
    发明申请
    NAVIGATION BY SPATIAL PLACEMENT OF SOUND 审中-公开

    公开(公告)号：US20190170533A1

    公开(公告)日：2019-06-06

    申请号：US16013927

    申请日：2018-06-20

    Applicant: EmbodyVR, Inc.

    Inventor： Kapil Jain

    IPC: G01C21/36 ,  H04S7/00 ,  H04R25/00

    Abstract: A direction of travel to a physical destination is determined. A sound signal is received. A signal is generated that spatializes sound in the direction of travel to the physical destination. The signal that spatializes the sound is output to the personal audio delivery device.

    System and method to virtually mix and audition audio content for vehicles
    4.
    发明授权
    System and method to virtually mix and audition audio content for vehicles 有权

    公开(公告)号：US11778408B2

    公开(公告)日：2023-10-03

    申请号：US17584984

    申请日：2022-01-26

    Applicant: EmbodyVR, Inc.

    Inventor： Kaushik Sunder ,  Marielle Venita Jakobsons ,  Kapil Jain

    IPC: H04S7/00 ,  H04R1/08 ,  H04R1/10 ,  H04R3/12

    CPC classification number: H04S7/303 ,  H04R1/08 ,  H04R1/1041 ,  H04R3/12 ,  H04R2499/13

    Abstract: A system comprises a sound source to output an audio signal to a plurality of speakers arranged in a plurality of locations in a vehicle. A laser device is disposed on an object representing a human head placed in a seat of the vehicle to scan the locations of the speakers. A microphone is disposed in each ear of the object. A controller routes the audio signal to the speakers one speaker at a time, receives audio signals received via the microphones, and compiles binaural acoustic data for the vehicle based on the audio signals received via the microphones. The controller receives scan data from the laser device and generates geometric data for the vehicle based on the scan data. The controller generates head-related transfer functions (HRTFs) for the object based on the binaural acoustic data and the geometric data for the vehicle.

    Personalized head related transfer function (HRTF) based on video capture
    5.
    发明授权
    Personalized head related transfer function (HRTF) based on video capture 有权

    公开(公告)号：US10701506B2

    公开(公告)日：2020-06-30

    申请号：US16138931

    申请日：2018-09-21

    Applicant: EmbodyVR, Inc.

    Inventor： Shruti Badhwar ,  Nikhil Ratnesh Javeri ,  Faiyadh Shahid ,  Kapil Jain

    IPC: H04S7/00 ,  H04S1/00

    Abstract: A video is received from a video capture device. The video capture device has a front facing camera and a display screen which displays the video captured by the video capture device in real time to a user. One or more images of a pinna and head of the user in the video are used to automatically determine one or more features associated with the user. The one or more features include an anatomy of the user, a demographic of the user, a latent feature of the user, and an indication of an accessory worn by the user. Based on the one or more features and one or more HRTF models, a head related transfer function (HRTF) is determined which is personalized to the user.

    AUTOMATED VERSIONING AND EVALUATION OF MACHINE LEARNING WORKFLOWS
    10.
    发明公开
    AUTOMATED VERSIONING AND EVALUATION OF MACHINE LEARNING WORKFLOWS 审中-公开

    公开(公告)号：US20230244999A1

    公开(公告)日：2023-08-03

    申请号：US18189762

    申请日：2023-03-24

    Applicant: EmbodyVR, Inc.

    Inventor： Shruti BADHWAR ,  Faiyadh Shahid ,  Nikhil Ratnesh Javeri ,  Kapil Jain

    IPC: G06N20/00 ,  G06F9/445

    CPC classification number: G06N20/00 ,  G06F9/44505

    Abstract: A method comprising determining a plurality of configurations for a machine learning workflow characterizing a pinna to predict a head related transfer function defining transformation of sound by the pinna. Each of the configurations comprises parameters for constructing and executing the machine learning workflow, a program code to be used for executing the machine learning workflow, and machine learning models to be used for tasks in the machine learning workflow. The method comprises generating a plurality of identifiers for the plurality of configurations, respectively, based on the machine learning models and the parameters by normalizing and encoding. The method comprises executing the machine learning workflow in accordance with the selected one of the configurations, analyzing results of the execution of the machine learning workflow to generate a metric, and storing results of the execution of the machine learning workflow along with the one of the identifiers and the metric.

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