This paper presents a method that determines the relevance of a set of signals (musical features) given listener judgments of music in an experimental setting. Rather than using linear correlation methods, we allow for nonlinear relationships and multi-dimensional feature vectors. We first provide a methodology based on polynomial functions and the least-mean-square error measure. We then extend the methodology to arbitrary nonlinear function approximation techniques and introduce the Kullback-Leibler Distance as an alternative relevance metric. The method is demonstrated first with simple artificial data and then applied to analyze complex experimental data collected to examine the perception of musical tension.