Singing voice detection in popular music

Abstract

We propose a novel technique for the automatic classification of vocal and non-vocal regions in an acoustic musical signal. Our technique uses a combination of harmonic content attenuation using higher level musical knowledge of key followed by sub-band energy processing to obtain features from the musical audio signal. We employ a Multi-Model Hidden Markov Model (MM-HMM) classifier for vocal and non-vocal classification that utilizes song structure information to create multiple models as opposed to conventional HMM training methods that employ only one model for each class. A statistical hypothesis testing approach followed by an automatic bootstrapping process is employed to further improve the accuracy of classification. An experimental evaluation on a database of 20 popular songs shows the validity of the proposed approach with an average classification accuracy of 86.7%.