ENHANCED AUDIOVISUAL SYNCHRONIZATION USING SYNTHESIZED NATURAL SIGNALS

Smart overview of the Invention

A method has been developed to improve audiovisual synchronization in video feeds, particularly during televised events. It involves receiving a video feed from a camera that includes synthesized content, which serves as a virtual representation of an object or being. By detecting delays between audio and video signals in this synthesized content, the system can generate and send corrected video content to another device for proper presentation.

Challenges in Current Synchronization Techniques

Current methods for ensuring audiovisual synchronization often rely on manual processes or pre-defined patterns that may not be tailored to specific events. These techniques can involve inserting recognizable signals, like QR codes or audio chirps, into the video feed to measure synchronization delays. However, such methods can be cumbersome and may not provide the flexibility needed for live events where conditions can change rapidly.

Synthesized Content for Improved Detection

The proposed system utilizes synthesized natural signals, such as a talking head or clapping sounds, to create recognizable patterns that are more relevant to the content being captured. This approach allows for real-time generation of synchronization cues specific to the event and eliminates the need for human operators to manually introduce these signals. The synthesized signals can be continuous, enhancing precision in detecting timing discrepancies between audio and video.

Implementation of Synthesized Signals

Synthesized content can be presented on various devices, which are then captured by cameras prior to the actual event being broadcasted. Different cameras may receive unique synthesized signals based on their specific needs, allowing for accurate synchronization analysis. Additionally, environmental details about the devices presenting this content can assist in further refining synchronization measurements.

Advanced Detection Methods

Various machine learning techniques may be employed to analyze audiovisual synchronization by transforming video into discrete objects and recognizing sound sources within the frames. Phoneme recognition and facial pattern classification are among the methods that can be used to correlate lip movements with expected sounds. The synthesized content can be generated using customizable digital avatars, ensuring that the synchronization process is both efficient and adaptable for different scenarios.