Download On the control of the phase of resonant filters with applications to percussive sound modeling Source-filter models are widely used in numerous audio processing fields, from speech processing to percussive/contact sound synthesis. The design of filters for these models—be it from scratch or from spectral analysis—usually involves tuning frequency and damping parameters and/or providing an all-pole model of the resonant part of the filter. In this context, and for the modelling of percussive (non-sustained) sounds, a source signal can be estimated from a filtered sound through a time-domain deconvolution process. The result can be plagued with artifacts when resonances exhibit very low bandwidth and lie very close in frequency. We propose in this paper a method that noticeably reduces the artifacts of the deconvolution process through an inter-resonance phase synchronization. Results show that the proposed method is able to design filters inducing fewer artifacts at the expense of a higher dynamic range.
Download Improved hidden Markov model partial tracking through time-frequency analysis In this article we propose a modification to the combinatorial hidden Markov model developed in [1] for tracking partial frequency trajectories. We employ the Wigner-Ville distribution and Hough transform in order to (re)estimate the frequency and chirp rate of partials in each analysis frame. We estimate the initial phase and amplitude of each partial by minimizing the squared error in the time-domain. We then formulate a new scoring criterion for the hidden Markov model which makes the tracker more robust for non-stationary and noisy signals. We achieve good performance tracking crossing linear chirps and crossing FM signals in white noise as well as real instrument recordings.
Download Generalization of the derivative analysis method to non-stationary sinusoidal modeling In the context of non-stationary sinusoidal modeling, this paper introduces the generalization of the derivative method (presented at the first DAFx edition) for the analysis stage. This new method is then compared to the reassignment method for the estimation of all the parameters of the model (phase, amplitude, frequency, amplitude modulation, and frequency modulation), and to the CramérRao bounds. It turns out that the new method is less biased, and thus outperforms the reassignment method in most cases for signalto-noise ratios greater than −10dB.