Distinguishing Monophonies From Polyphonies Using Weibull Bivariate Distributions

Abstract

In the context of music indexation, it would be useful to have a precise information about the number of sources performing; a source is a solo voice or an isolated instrument which produces a single note at any time. This correspondence discusses the automatic distinction between monophonic music excerpts, where only one source is present, and polyphonic ones. Our method is based on the analysis of a “confidence indicator,” which gives the confidence (in fact its inverse) on the current estimated fundamental frequency (pitch). In a monophony, the confidence indicator is low. In a polyphony, the confidence indicator is higher and varies more. This leads us to compute the short term mean and variance of this indicator, take this 2-D vector as the observation vector and model its conditional distribution with Weibull bivariate models. This probability density function is characterized by five parameters. A method to perform their estimation is developed (in theory and practice). The decision is taken considering the maximum likelihood, computed over one second. The best configuration gives a global error rate of 6.3%, performed on a balanced corpus (18 minutes in total).