Event localization prediction accuracy (%).

The growing reliance on video conferencing software brings significant benefits but also introduces challenges, particularly in managing audio quality. In multi-participant settings, ambient noise and interruptions can hinder speaker recognition and disrupt the flow of conversation. This work proposes an audio-visual source separation pipeline designed specifically for video conferencing and telepresence robots applications. The framework aims to isolate and enhance the speech of individual participants in noisy environments while enabling control over the volume of specific individuals captured in the video frame. The proposed pipeline comprises key components: a deep learning-based feature extractor for audio and video, an audio-guided visual attention mechanism, a module for background noise suppression and human voice separation, and Deep Multi-Resolution Network (DMRN) modules. For human voice separation, the DPRNN-TasNet, a robust deep neural network framework, is employed. Experimental results demonstrate that the methodology effectively isolates and amplifies individual participants’ speech, achieving a test accuracy of 71.88 % on both the AVE and Music 21 datasets.