Disney Enterprises, Inc.

Mitchel C Jacobs

Anil Chakrahari

Christopher Richard Schroers

Scott C Labrozzi

Michael Andrew Radford

Pierre-Anthony Stivell Lemieux

Michael Smith

Benjamin Eric Pearson

Christian Parker

Larry Barker

Cafer Mertcan Akcay

Eitan Abecassis

Ellie Kuang

Joan Massich Vall

Katherine Hinsen

Clara Fernandez Labrador

Michael Fernandez

A system includes a hardware processor and a memory storing a video/audio (V/A) synchronizer including video and audio encoders. The hardware processor executes the V/A synchronizer to receive raw video and audio extracted from media content, partition the raw video into video frame patches, partition the raw audio into audio samples, pre-process the video frame patches and the audio samples for encoding. The hardware processor further executes the V/A synchronizer to encode, using the video encoder, the pre-processed video frame patches to provide pre-processed and encoded video frame patches used to provide a latent representation of the raw video, encode, using the audio encoder, the pre-processed audio samples to provide pre-processed and encoded audio samples used to provide a latent representation of the raw audio, and synchronize, using the latent representations of the raw video and the raw audio, the raw audio with the raw video.

Video and audio synchronization with dynamic frame and sample rates

A system includes a hardware processor, and a memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types. The hardware processor executes the software code to receive a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predict, using the at least one ML model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the data; and determine, based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first content type identified by the data, or (iii) is of an indeterminate content type.

Machine learning model-based detection of content type

A differential media uploading system includes a hardware processor and a memory storing software code. The hardware processor executes the software code to receive a timeline file including a playlist track for a video file, and determine whether any pre-existing video frames stored in a media mastering system include a respective image depicted by a video frame contained in the video file. When it is determined that any of the pre-existing video frames include the respective image, the hardware processor executes the software code to disregard the video frame. When it is determined that none of the pre-existing video frames include the respective image, the hardware processor executes the software code to encode the video frame to provide an encoded video frame and upload the encoded video frame to the media mastering system.

Automation of differential media uploading

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Malibu, CA, United States of America

Mitchel C Jacobs