Content Discovery from Composite Audio: An unsupervised approach

In this thesis, we developed and assessed a novel robust and unsupervised framework for semantic inference from composite audio signals. We focused on the problem of detecting audio scenes and grouping them into meaningful clusters. Our approach addressed all major steps in a general process of composite audio analysis, from low-level signal...