This paper is about multiple-F0 tracking and the estimation of the number of harmonic source streams in music sound signals. A source stream is understood as generated from a note played by a musical instrument. A note is described by a hidden Markov model (HMM) having two states: the attack state and the sustain state. It is proposed to first perform the tracking of F0 candidates using a high-order hidden Markov model, based on a forward-backward dynamic programming scheme. The propagated weights are calculated in the forward tracking stage, followed by an iterative tracking of the most likely trajectories in the backward tracking stage. Then, the estimation of the underlying source streams is carried out by means of iteratively pruning the candidate trajectories in a maximum likelihood manner. The proposed system is evaluated by a specially constructed polyphonic music database. Compared with the frame-based estimation systems, the tracking mechanism improves significantly the accuracy rate.

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Recently, the processing of non-sinusoidal signals, or sound textures, has become an important topic in various areas. In general, the transformation is done by the phase vocoder techniques. Since the phase vocoder technique is based on a sinusoidal model, it’s performance is not satisfying when applied to transform sound textures. The following article investigates into the problem using as example the most basic non-sinusoidal sounds, that are noise signals. We demonstrate the problems that arise when time stretching noise with the phase vocoder, provide a description of some relevant statistical properties of the time frequency representation of noise and introduce an algorithm that allows to preserve these statistical properties when time stretching noise with the phase vocoder. The resulting algorithm significantly improves the perceptual quality of the time stretched noise signals and therefore it is seen as a promising first step towards an algorithm for transformation of sound textures.

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