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BATON LipSync Automatic Audio-Video Sync Detection

Post Production - QC/QA

DMW Awards Gold

Interra Systems’ BATON® LipSync is a powerful audio-video synchronization tool for lip sync detection and verification. Leveraging image processing and machine learning, BATON LipSync automatically detects audio-video sync errors in media content, helping broadcasters and service providers deliver a superior quality of experience (QoE) and eliminate what can be a common and annoying problem for viewers.

Interra Systems' LipSync solution has a unique capability of performing facial detection, facial tracking, lip detection, lip activity detection, and speech identification. Compared with the traditional approach of manually checking for lip sync errors, the ML-based method is much faster and more precise. Sync errors can be debugged further in the BATON Media Player (BMP) through an 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 errors.

Available content can be checked in any language independent of the region and area. This ensures that broadcasters and service providers are able to respond to the growing need for global content.

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, media companies can expedite content preparation, by converting what was once a time-consuming and expensive process, into an efficient, accurate, and cost effective process.

BATON LipSync deserves to win this award because it answers a critical industry need by replacing a time-consuming, expensive manual process with faster and more accurate machine-assisted detection.