Download Sinusoidal Parameter Extraction and Component Selection in a non Stationary Model In this paper, we introduce a new analysis technique particularly suitable for the sinusoidal modeling of non-stationary signals. This method, based on amplitude and frequency modulation estimation, aims at improving traditional Fourier parameters and enables us to introduce a new peak selection process, so that only peaks having coherent parameters are considered in subsequent stages (e.g. partial tracking, synthesis). This allows our spectral model to better handle natural sounds.
Download Enhanced partial tracking using linear prediction In this paper, we introduce a new partial tracking method suitable for the sinusoidal modeling of mixtures of instrumental sounds with pseudo-stationary frequencies. This method, based on the linear prediction of the frequency evolutions of the partials, enables us to track these partials more accurately at the analysis stage, even in complex sound mixtures. This allows our spectral model to better handle polyphonic sounds.
Download Improving Sinusoidal Frequency Estimation Using a Trigonometric Approach Estimating the frequency of sinusoidal components is the first part of the sinusoidal analysis chain. Among numerous frequency estimators presented in the literature, we propose to study an estimator proposed in [1] known as the derivative algorithm. Thanks to a trigonometric interpretation of this frequency estimator, we are able to propose a new estimator which improves estimation performance for the frequencies close to the Nyquist frequency without any computational overload.