Separation of music signal spectrograms using copula models

The focus of this paper is on utilizing a copula-based technique for separating music signals. In this method, copulas are utilized to model the dependency structure of the source components. The objective is to minimize the Kullback-Leibler divergence between the copula density of the estimated source components and the copula density of the source components, which is assumed to be unknown. We give an application for the separation of stereo music recording using different copula models. We show through this application that by modeling the dependency between music signals, we can gain insights into the underlying structure of music signals and also we can ameliorate the separation performance.