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.

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In the following paper we investigate into the estimation of sinusoidal parameters for sinusoids with linear AM/FM modulation. It will be shown that for linear amplitude and frequency modulation only the frequency modulation creates additional estimation bias for the standard sinusoidal parameter estimator. Then an enhanced algorithm for frequency domain demodulation of spectral peaks is proposed that can be used to obtain an approximate maximum likelihood estimate of the frequency slope, and an estimate of the amplitude, phase and frequency parameter with significantly reduced bias. An experimental evaluation compares the new estimation scheme with previously existing methods. It shows that significant bias reduction is achieved for a large range of slopes and zero padding factors. A real world example demonstrates that the enhanced bias reduction algorithm can achieve a reduction of the residual energy of up to 9dB.

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