A comparative study in automatic recognition of broadcast audio

This paper provides a thorough description of a methodology which leads to high accuracy as regards automatic analysis of broadcast audio. The main objective is to find a feature set for efficient speech/music discrimination while keeping the number of its dimensions as small as possible. Three groups of parameters based on Mel-scale filterbank, MPEG-7 standard and wavelet decomposition are examined in detail. We annotated on-line radio recordings characterized by great diversity, for building probabilistic models and testing four frameworks. The proposed approach utilizes wavelets and MPEG-7 ASP descriptor for modeling speech and music respectively, and results to 98.5 % average recognition rate.