Plagiarism risk detector and interface

    公开(公告)号:US11289059B2

    公开(公告)日:2022-03-29

    申请号:US16802308

    申请日:2020-02-26

    Applicant: Spotify AB

    Abstract: Methods, systems and computer program products are provided for testing a lead sheet for plagiarism. A test lead sheet receiving having a plurality of passages is received at receiving a plagiarism detector. A set of annotations describing a level of plagiarism of a plurality of elements (e.g., chord sequence, subsequences, melodic fragments (i.e., notes), rhythm, harmony, etc.) of the test lead sheet in relation to the preexisting lead sheets are generated and output via an output device.

    Skip behavior analyzer
    12.
    发明授权

    公开(公告)号:US11256469B2

    公开(公告)日:2022-02-22

    申请号:US16591019

    申请日:2019-10-02

    Applicant: Spotify AB

    Abstract: A skip behavior analyzer is part of a media delivery system that allows for unbiased A/B testing of a plurality of versions of a song. The media delivery system stores a plurality of versions of a song and randomly selects, for each requesting device, a version of the song to associate with that device. Each time the device requests the song, thereafter, the media delivery system will provide the same version of the song for consistency. The media delivery system then gathers song play and skip information, calculates the differences in distribution of the skip behavior, and provides the skip information to allow a music composer to better determine which version of a song is more popular and why that is so.

    DEVICE FOR EFFICIENT USE OF COMPUTING RESOURCES BASED ON USAGE ANALYSIS

    公开(公告)号:US20200272659A1

    公开(公告)日:2020-08-27

    申请号:US16285305

    申请日:2019-02-26

    Applicant: Spotify AB

    Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

    Automatic preparation of a new MIDI file

    公开(公告)号:US11676565B2

    公开(公告)日:2023-06-13

    申请号:US17080654

    申请日:2020-10-26

    Applicant: Spotify AB

    CPC classification number: G10H5/02

    Abstract: The present disclosure relates to a method of automatically preparing a MIDI file based on a target MIDI file comprising respective note information about each of a plurality of target notes and a source MIDI file comprising respective note information about each of a plurality of source notes. Each note information comprises pitch information defining a pitch of the note. The method comprises ranking the plurality of target notes based on the pitch of each target note. The method also comprises, for each of the ranked target notes, removing the pitch information from the note information of the target note. The method also comprises, for each of the ranked target notes, replacing the removed pitch information with pitch information of a corresponding source note, whereby the target note has the same pitch as the corresponding source note, forming a plurality of new notes of a new MIDI file.

    Methods, systems and computer program products for generating a training set for use during content generation

    公开(公告)号:US11593702B2

    公开(公告)日:2023-02-28

    申请号:US16434668

    申请日:2019-06-07

    Applicant: Spotify AB

    Abstract: A training set for use during content generation is generated by applying a first machine learning process P1 to a first finite sequence s wherein s has a length Ls, to generate a first statistical model M(s). The first statistical model M(s) is sampled using a first sampling process G to generate a second finite sequence t wherein t has a length Lt. A second machine learning process P2 is applied to the second finite sequence t to generate a second statistical model M(t), wherein no substring of the second finite sequence t of length d is identical to a substring of the first finite sequence s, wherein d is a predetermined number of elements in a sequence.

    USER CONSUMPTION BEHAVIOR ANALYSIS AND COMPOSER INTERFACE

    公开(公告)号:US20220335084A1

    公开(公告)日:2022-10-20

    申请号:US17722800

    申请日:2022-04-18

    Applicant: Spotify AB

    Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

    User consumption behavior analysis and composer interface

    公开(公告)号:US11341184B2

    公开(公告)日:2022-05-24

    申请号:US16285305

    申请日:2019-02-26

    Applicant: Spotify AB

    Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

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