Gaussian Mixture Models For Extraction Of Melodic Lines From Audio Recordings

The presented study deals with extraction of melodic line(s) from polyphonic audio recordings. We base our work on the use of expectation maximization algorithm, which is employed in a two-step procedure that finds melodic lines in audio signals. In the first step, EM is used to find regions in the signal with strong and stable pitch (melodic fragments). In the second step, these fragments are grouped into clusters according to their properties (pitch, loudness...). The obtained clusters represent distinct melodic lines. Gaussian Mixture Models, trained with EM are used for clustering. The paper presents the entire process in more detail and gives some initial results.