Netflix Inc.

Ebay, Incorporated

Shilpa Jois Rao

Yadong Wang

Kyle Tacke

Andrew Kerr Swan

Raja Ranjan Senapati

Dong Chen Chen

Murthy Parthasarathi

Seyed-Mahdi Pedramrazi

Chih-Wei Wu

Boney Sekh

Shaumik Chandra Mondal

James L Grubbs, Jr

Subramanian Sri Sankaran

Bryan Ephraim Freeland

Rita Marion Bosch

Anjali Chablani

The disclosed computer-implemented method includes analyzing, by a speech detection system, a media file to detect lip movement of a speaker who is visually rendered in media content of the media file. The method additionally includes identifying, by the speech detection system, audio content within the media file, and improving accuracy of a temporal correlation of the speech detection system. The method may involve correlating the lip movement of the speaker with the audio content, and determining, based on the correlation between the lip movement of the speaker and the audio content, that the audio content comprises speech from the speaker. The method may further involve recording, based on the determination that the audio content comprises speech from the speaker, the temporal correlation between the speech and the lip movement of the speaker as metadata of the media file. Various other methods, systems, and computer-readable media are disclosed.

Systems and methods for correlating speech and lip movement

The disclosed computer-implemented method may include systems and methods for automatically generating sound event subtitles for digital videos. For example, the systems and methods described herein can automatically generate subtitles for sound events within a digital video soundtrack that includes sounds other than speech. Additionally, the systems and methods described herein can automatically generate sound event subtitles as part of an automatic and comprehensive approach that generates subtitles for all sounds within a soundtrack of a digital video—thereby avoiding the need for any manual inputs as part of the subtitling process.

Systems and methods for automatically generating sound event subtitles

The disclosed computer-implemented method may include receiving, as input, an audio/video data object; isolating a video stream of a visible potential speaker over a plurality of frames of the audio/video data object; isolating an audio stream over the plurality of frames; providing the isolated video stream and the isolated audio stream to a machine learning model trained with contrastive learning, the contrastive learning using (i) a corpus of video segments of visible speakers with corresponding original audio for positive samples; and (ii) a corpus of video segments of visible speakers with corresponding dubbed audio for negative samples; and evaluating a match between the isolated audio stream and the isolated video stream based at least in part on an output of the machine learning model. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for active speaker detection

The disclosed computer-implemented method may include (1) accessing a first media data object and a different, second media data object that, when played back, each render temporally sequenced content, (2) comparing first temporally sequenced content represented by the first media data object with second temporally sequenced content represented by the second media data object to identify a set of common temporal subsequences between the first media data object and the second media data object, (3) identifying a set of edits relative to the set of common temporal subsequences that describe a difference between the temporally sequenced content of the first media data object and the temporally sequenced content of the second media data object, and (4) executing a workflow relating to the first media data object and/or the second media data object based on the set of edits. Various other methods, systems, and computer-readable media are also disclosed.

Automated workflows from media asset differentials

Visual diagram searching techniques are described herein. A visual diagram service enables users to efficiently search for data for item parts even in cases where the name of the item part is unknown. In one or more examples, search query input to locate item parts of an item is received via a user interface displayed by at least one computing device. A visual diagram of the item is displayed in the user interface. The visual diagram includes selectable portions mapped to respective item parts depicted in the corresponding selectable portion of the visual diagram. A user selection of one of the selectable portions of the visual diagram of the item is received via the user interface. In response to the user selection, search result data corresponding to the respective item part depicted in the selected selectable portion of the visual diagram of the item is displayed.

Method, computer readable storage devices, and computer device for mapping visual diagram portions to parts listing information

The disclosed computer-implemented method may include accessing an audio track that is associated with a video recording, identifying a section of the accessed audio track having a specific audio characteristic, reducing a volume level of the audio track in the identified section, accessing an audio segment that includes a synthesized voice and inserting the accessed audio segment into the identified section of the audio track, where the inserted segment has a higher volume level than the reduced volume level of the audio track in the identified section. The synthesized voice description can be used to provide additional information to a visually impaired viewer without interrupting the audio track that is associated with the video recording, typically by inserting the synthesized voice description into a segment of the audio track in which there is no dialog. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for mixing synthetic voice with original audio tracks

The disclosed computer-implemented method may include obtaining an audio sample from a content source, inputting the obtained audio sample into a trained machine learning model, obtaining the output of the trained machine learning model, wherein the output is a profile of an environment in which the input audio sample was recorded, obtaining an acoustic impulse response corresponding to the profile of the environment in which the input audio sample was recorded, obtaining a second audio sample, processing the obtained acoustic impulse response with the second audio sample, and inserting a result of processing the obtained acoustic impulse response and the second audio sample into an audio track. Various other methods, systems, and computer-readable media are also disclosed.

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Cupertino, CA, United States of America

Shilpa Jois Rao