The SyncNet Research Paper, Clearly Explained

The SyncNet research paper introduces a self-supervised method to automatically detect and correct audio-video synchronization problems. It employs a dual-stream convolutional neural network (CNN) architecture, with separate branches for processing audio (as MFCCs) and video (mouth regions). The model learns a joint embedding space where synced audio-visual clips are mapped closely together, trained using a contrastive loss function. This approach allows the system to determine the precise time offset between audio and video without manual labels. Key applications include fixing lip-sync errors in broadcasts and identifying active speakers in a scene.