AUTOMATIC CLASSIFICATION OF ELECTRONIC MUSIC AND SPEECH/MUSIC AUDIO CONTENT BY

Automatic audio categorization has great potential for application in the maintenance and usage of large and constantly growing media databases; accordingly, much research has been done to demonstrate the feasibility of such methods. A popular topic is that of automatic genre classification, accomplished by training machine learning algorithms. However, “electronic ” or “techno ” music is often misrepresented in prior work, especially given the recent rapid evolution of the genre and subsequent splintering into distinctive subgenres. As such, features are extracted from electronic music samples in an experiment to categorize song samples into three subgenres: deep house, dubstep, and progressive house. An overall classification performance of 80.67% accuracy is achieved, comparable to prior work. Similarly, many past studies have been conducted on speech/music discrimination due to the potential applications for broadcast and other media, but it remains possible to expand the experimental scope to include samples of speech with varying amounts of background music. The development and evaluation of two measures of the ratio between speech energy and music energy are explored: a reference measure called speech-to-music ratio (SMR) and a feature which is an imprecise estimate of SMR called estimated voice-to-music ratio (eVMR). eVMR i