Development of a Reference Platform for Generic Audio Classification

Detection of key sounds, such as applause, laugh, music, environmental noise, etc., is one of the challenges in intelligent management of multimedia information and content understanding. In this paper, we report progress in development of a reference content-based audio classification algorithm that is based on a conventional and widely accepted approach, namely signal parameterization by MFCC followed by GMM classification. Our developed labeled audio database and the conventional classification model should serve as a reference platform for an evaluation of novel, alternative or more advanced methods in audio content analysis.