Adaptive Threshold Determination for Spectral Peak Classification

Miroslav Zivanovic; Axel Röbel; Xavier Rodet
DAFx-2007 - Bordeaux
A new approach to adaptive threshold selection for classification of peaks of audio spectra is presented. We here extend the previous work on classification of sinusoidal and noise peaks based on a set of spectral peak descriptors in a twofold way: on one hand we propose a compact sinusoidal model where all the modulation parameters are defined with respect to the analysis window. This fact is of great importance as we recall that the STFT spectra are closely related to the analysis window properties. On the other hand, we design a threshold selection algorithm that allows us to control the decision thresholds in an intuitive manner. The decision thresholds calculated from the relationships established between the noise power in the signal and the distributions of sinusoidal peaks assures that all peaks described as sinusoidal will be correctly classified. We also show that the threshold selection algorithm can be used for different types of analysis windows with only a slight parameter readjustment.