An experimental approach to generalized Wiener filtering in music source separation
Music source separation aims at decomposing music recordings into their constituent component signals. Many existing techniques are based on separating a time-frequency representation of the mixture signal by applying suitable modeling techniques in conjunction with generalized Wiener filtering. Recently, the term a-Wiener filtering was coined together with a theoretic foundation for the long-practiced use of magnitude spectrogram estimates in Wiener filtering. So far, optimal values for the magnitude exponent a have been empirically found in oracle experiments regarding the additivity of spectral magnitudes. In the first part of this paper, we extend these previous studies by examining further factors that affect the choice of a. In the second part, we investigate the role of a in Kernel Additive Modeling applied to Harmonic-Percussive Separation. Our results indicate that the parameter a may be understood as a kind of selectivity parameter, which should be chosen in a signal-adaptive fashion.