The Directv Group, Inc.

At&t Intellectual Property I, L.p.

At&t Mobility Ii LLC

Kevin Culwell

Daniel M Miner

James A Michener

Leon J Stanger

Heather Truong

Uy X Nguyen

Manuel Briand

Eric Petajan

Xavier Riley

Arlene Cava

Lucian Jiang-Wei

Nicholas Covey

Esteban Gomez

Rajakumar Adhikari

Concepts and technologies disclosed herein are directed to audio and video synchronization. According to one aspect disclosed herein, an audio-video ('AV') synchronization system can simultaneously capture samples of a pre-encode media stream and a post-encode media stream. The pre-encode media stream can include AV content prior to being encoded. The post-encode media stream can include the AV content after being encoded. The AV synchronization system can align a pre-encode video component of the pre-encode media stream with a post-encode video component and can determine a video offset therebetween. The AV synchronization system can align a pre-encode audio component of the pre-encode media stream with a post-encode audio component of the post-encode media stream and can determine an audio offset therebetween. The AV synchronization system can then compare the video offset and the audio offset to determine if the post-encode media stream is synchronized with the pre-encode media stream.

Audio and video synchronization

An audio loudness data collection module collects and stores audio data. The data can be stored for a first time period. A monitoring system can access and store the audio data collected and stored by the loudness data collection module according to a second time period. The stored audio data can be processed and displayed to a user in a number of ways. The data can also be used to provide loudness data control. For example, a loudness data control module can perform an audio AGC function to control audio loudness.

System and method to monitor audio loudness and provide audio automatic gain control

A garbled closed captioning detector analyzes closed captioning data at predetermined intervals to determine whether a metric derived from the closed-captioning data exceeds a threshold. If the metric exceeds the predetermined threshold, the closed captioning data is presumed to be garbled. For example, the metric may be the percentage of words in the closed captioning data having a desired word length observed in a particular interval compared to all words in the closed captioning data observed in the interval. If the percentage of words having a desired word length exceeds a predetermined threshold, the closed captioning data is presumed to be garbled.

System and method to detect garbled closed captioning

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Redondo Beach, CA, United States of America

Kevin Culwell