Real-World Sound Recognition: A Recipe
Tjeerd Andringa, Maria E. Niessen, A Nurnberger, S Stober, P. Cano, George Tzanetakis · 2006
Abstract. This article addresses the problem of recognizing acoustic events present in unconstrained input. We propose a novel approach to the processing of audio data which combines bottom-up hypothesis generation with top-down expectations, which, unlike standard pattern recognition techniques, can ensure that the representation of the input sound is physically realizable. Our approach gradually enriches low-level signal descriptors, based on Continuity Preserving Signal Processing, with more and more abstract interpretations along a hierarchy of descrip-tion levels. This process is guided by top-down knowledge which provides context and ensures an interpretation consistent with the knowledge of the system. This leads to a system that can detect and recognize spe-cific events for which the evidence is present in unconstrained real-world sounds.