Interra Systems has launched Baton LipSync, an automated tool for lip sync detection and verification.
Baton LipSync leverages machine learning (ML) technology and deep neural networks to automatically detect audio and video sync errors.
Using Baton LipSync, broadcasters and service providers can accurately detect audio lead and lag issues in media content in order to provide a superior quality of experience to viewers.
"Lip sync errors are annoying and distracting and negatively impact overall viewing experiences," said Anupama Anantharaman, vice president of product management at Interra Systems.
"With our BAton LipSync product, we're extending our video quality control expertise to help address the difficult problem of audio-video sync using advanced image processing and machine learning technologies. In addition to solving the problem of detection, accompanying debug information and comprehensive reporting will go a long way in assuring audio-video sync accuracy and helping our customers to deliver a more satisfying viewing experience."
Interra System's LipSync application is capable of performing facial detection, facial tracking, lip detection, and lip activity detection.
Sync errors can be debugged further in the Baton Media Player through a feature-rich interface that plots out-of-sync audio and video errors on a skew timeline for better visualization. After errors are detected, Baton LipSync provides a comprehensive report of all the lip sync issues.
Available content can be checked in any language independent of the region and area.
Offering support for most industry formats, Baton LipSync meets media professionals' complex requirements for delivering high-quality multiscreen video. Baton LipSync can be seamlessly integrated into any existing content processing workflow, or it can be used as a stand-alone application.
By augmenting their current manual workflows with an ML-powered solution for lip sync detection and verification, broadcasters can increase their efficiency. Baton LipSync answers a critical industry need by replacing a time-consuming, expensive manual process with faster and more accurate machine-assisted detection.