Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics

This study presents an unsupervised alignment architecture using a supervised learning model to align heterogeneous data with unknown semantic time shifts. It demonstrates effectiveness in aligning optical and acoustic signals, enabling semantic mining and in…