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    Method, system, and computer-readable medium for creating song mashups
    1.
    发明授权
    Method, system, and computer-readable medium for creating song mashups 有权

    公开(公告)号：US12254855B2

    公开(公告)日：2025-03-18

    申请号：US17930933

    申请日：2022-09-09

    Applicant: Spotify AB

    Inventor： Juan José Bosch Vicente ,  Youn Jin Kim ,  Peter Milan Thomson Sobot ,  Angus William Sackfield

    IPC: G10H1/00

    Abstract: A system, method and computer product for combining audio tracks. In one example embodiment herein, the method comprises determining at least one music track that is musically compatible with a base music track, aligning those tracks in time, and combining the tracks. In one example embodiment herein, the tracks may be music tracks of different songs, the base music track can be an instrumental accompaniment track, and the at least one music track can be a vocal track. Also in one example embodiment herein, the determining is based on musical characteristics associated with at least one of the tracks, such as an acoustic feature vector distance between tracks, a likelihood of at least one track including a vocal component, a tempo, or musical key. Also, determining of musical compatibility can include determining at least one of a vertical musical compatibility or a horizontal musical compatibility among tracks.

    Access control for on-device machine learning models
    2.
    发明授权
    Access control for on-device machine learning models 有权

    公开(公告)号：US12204663B2

    公开(公告)日：2025-01-21

    申请号：US17241383

    申请日：2021-04-27

    Applicant: Spotify AB

    Inventor： Peter Milan Thomson Sobot ,  David Samuel Rubinstein

    IPC: G06F21/62 ,  G06N20/00

    Abstract: A system and method for controlling access to an on-device machine learning model without the use of encryption is described herein. For example, a request is received from an application executing on a device of a user. The request is to download a machine learning model to the device that enables a feature of the application, and the request includes information associated with the user and/or the device. The information is used to create an obfuscation key, and a derivative model can be generated using a reference copy of the machine learning model and the obfuscation key. The derivative model and the obfuscation key are then sent to the application. When the obfuscation key is provided to the derivative model at runtime, values derived from the obfuscation key are provided as additional inputs that enable the derivative model to function properly.

    Method, system, and computer-readable medium for creating song mashups
    4.
    发明授权
    Method, system, and computer-readable medium for creating song mashups 有权

    公开(公告)号：US11475867B2

    公开(公告)日：2022-10-18

    申请号：US16728953

    申请日：2019-12-27

    Applicant: Spotify AB

    Inventor： Juan José Bosch Vicente ,  Youn Jin Kim ,  Peter Milan Thomson Sobot ,  Angus William Sackfield

    IPC: G10H1/08 ,  G10H1/00

    Abstract: A system, method and computer product for combining audio tracks. In one example embodiment herein, the method comprises determining at least one music track that is musically compatible with a base music track, aligning those tracks in time, and combining the tracks. In one example embodiment herein, the tracks may be music tracks of different songs, the base music track can be an instrumental accompaniment track, and the at least one music track can be a vocal track. Also in one example embodiment herein, the determining is based on musical characteristics associated with at least one of the tracks, such as an acoustic feature vector distance between tracks, a likelihood of at least one track including a vocal component, a tempo, or musical key. Also, determining of musical compatibility can include determining at least one of a vertical musical compatibility or a horizontal musical compatibility among tracks.

    Systems and methods for embedding data in media content
    6.
    发明授权
    Systems and methods for embedding data in media content 有权

    公开(公告)号：US10777177B1

    公开(公告)日：2020-09-15

    申请号：US16588470

    申请日：2019-09-30

    Applicant: Spotify AB

    Inventor： Andreas Jansson ,  David Samuel Rubinstein ,  Peter Milan Thomson Sobot

    IPC: G10H1/00 ,  G10H1/26 ,  H04S1/00 ,  G06F16/683

    Abstract: An electronic device determines a first audio event of a first media content item and modifies the first media content item by superimposing a first set of data that corresponds to the first media content item over the first audio event. The first audio event has a first audio profile configured to be presented over a first channel for playback. The first set of data has a second audio profile configured to be presented over the first channel for playback. Playback of the second audio profile is configured to be masked by the first audio profile during playback of the first media content item. The electronic device transmits, to a second electronic device, the modified first media content item.

    Access Control for On-Device Machine Learning Models
    7.
    发明申请
    Access Control for On-Device Machine Learning Models 有权

    公开(公告)号：US20250111065A1

    公开(公告)日：2025-04-03

    申请号：US18976868

    申请日：2024-12-11

    Applicant: Spotify AB

    Inventor： Peter Milan Thomson Sobot ,  David Samuel Rubinstein

    IPC: G06F21/62 ,  G06N20/00

    Abstract: A system and method for controlling access to an on-device machine learning model without the use of encryption is described herein. For example, a request is received from an application executing on a device of a user. The request is to download a machine learning model to the device that enables a feature of the application, and the request includes information associated with the user and/or the device. The information is used to create an obfuscation key, and a derivative model can be generated using a reference copy of the machine learning model and the obfuscation key. The derivative model and the obfuscation key are then sent to the application. When the obfuscation key is provided to the derivative model at runtime, values derived from the obfuscation key are provided as additional inputs that enable the derivative model to function properly.

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