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公开(公告)号:US11327710B2
公开(公告)日:2022-05-10
申请号:US16832883
申请日:2020-03-27
Applicant: Adobe Inc.
Inventor: Nico Becherer , Sven Duwenhorst
Abstract: A computer-implemented method for audio signal processing includes analyzing a foreground audio signal to determine metrics corresponding to audio slices of the foreground audio signal. Each such metric indicates a value for an audio property of a respective audio slice. The method further includes computing a total metric for an audio slice as a function of a set of the metrics corresponding to a set of the audio slices including the audio slice. The method further includes adding a key frame to a track based on the total metric. The track includes the foreground audio signal and a background audio signal, and a location of the key frame corresponds to a location of the audio slice on the track. The key frame indicates a change to the audio property of the background audio signal at the location on the track, and the key frame is utilizable for audio ducking.
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公开(公告)号:US10262694B2
公开(公告)日:2019-04-16
申请号:US15875158
申请日:2018-01-19
Applicant: Adobe Inc.
Inventor: Nico Becherer
IPC: G11B27/038 , G11B27/34 , G11B27/036
Abstract: Average pixel luminosity is calculated for each frame comprising a content item. For each pair of adjacent frames, an IFD is calculated. The IFD represents the difference between a baseline pixel luminosity associated with each of the two frames. An initial set of cut frames is selected based on IFD values that are less than a minimum value IFDmin, or that are greater than a maximum value IFDmax. The positions of these initial cut frames are optimized using a numerical optimization technique that favors removal of frames corresponding to IFD extrema, but that also attempts to maintain a minimum time gap between cut frames. Selecting frames for removal is approached as a constraint minimization problem. Once an optimized set of cut frames is established, audio is cut and crossfaded in a temporal window surrounding cut frame positions.
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公开(公告)号:US12153705B2
公开(公告)日:2024-11-26
申请号:US17217024
申请日:2021-03-30
Applicant: ADOBE INC.
Inventor: William Marino , Tim Converse , Sudharshan reddy Kakumanu , Shabnam Ghadar , Nico Becherer , Dhaval Shah , Ben Bowles , Alvin Ghouas , Alexander Riss
Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.
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公开(公告)号:US20220318420A1
公开(公告)日:2022-10-06
申请号:US17217024
申请日:2021-03-30
Applicant: ADOBE INC.
Inventor: William Marino , Tim Converse , Sudharshan reddy Kakumanu , Shabnam Ghadar , Nico Becherer , Dhaval Shah , Ben Bowles , Alvin Ghouas , Alexander Riss
Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.
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