Netflix Inc.

Verizon Patent and Licensing Inc.

Synecor LLC

Yadong Wang

Shilpa Jois Rao

Kyle Tacke

Murthy Parthasarathi

Andrew Kerr Swan

Boney Sekh

Raja Ranjan Senapati

Chih-Wei Wu

Michael Scott Williams

Andrew Walters

Ravi Kalluri

Youhong Lu

Thomas E Mack

Robert S Langer

Luigi Bojan

Vasanthi Vuppuluri

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

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.

System and methods for automatically mixing audio for acoustic scenes

In various embodiments, a subtitle timing application detects timing errors between subtitles and shot changes. In operation, the subtitle timing application determines that a temporal edge associated with a subtitle does not satisfy a timing guideline based on a shot change. The shot change occurs within a sequence of frames of an audiovisual program. The subtitle timing application then determines a new temporal edge that satisfies the timing guideline relative to the shot change. Subsequently, the subtitle timing application causes a modification to a temporal location of the subtitle within the sequence of frames based on the new temporal edge. Advantageously, the modification to the subtitle improves a quality of a viewing experience for a viewer. Notably, by automatically detecting timing errors, the subtitle timing application facilitates proper and efficient re-scheduling of subtitles that are not optimally timed with shot changes.

Detecting errors in the timing between subtitles and shot changes

In various embodiments, a subtitle application generates a subtitle list for a trailer. In operation, the subtitle application performs matching operation(s) between trailer audio associated with a trailer and source audio associated with an audiovisual program. The subtitle application then maps a subtitle associated with the source audio from a source timeline associated with the source audio to a trailer timeline associated with the trailer audio to generate a mapped subtitle. Subsequently, the subtitle application generates a trailer subtitle list based on the mapped subtitle and at least one additional mapped subtitle. Because the subtitle application generates the trailer subtitle list based on audio comparisons, the subtitle application ensures that the proper subtitles are included in the trailer subtitle list without requiring a subtitler to view the trailer.

Techniques for generating subtitles for trailers

Techniques, described herein, may enable a computing device (e.g., a set-top-box, a user equipment (UE), etc.) with an array of sensors (e.g., microphones, antennas, etc.) to engage in beamforming in a manner that optimizes the signal strength of the target signal. For example, the computing device may create an optimized beam pattern by applying a pre-selected band width to a steering direction of the target signal. The beam width may include steering vectors at equal, angular distances from the steering direction, enabling steep transition slopes to be applied to the optimized beam pattern. In some implementations, the beam width and transition slopes may be standardized, such that the optimized beam pattern for any signal may be uniform, regardless of variables such as frequency and time.

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Campbell, CA, United States of America

Yadong Wang