In this paper a new algorithm to compute an additive synthesis model of a signal is presented. An analysis based on the Continuous Wavelet Transform (CWT) has been used to extract the time-varying amplitudes and phases of the model. A coarse to fine analysis increases the algorithm efficiency. The computation of the transient analysis is performed using the same algorithm developed for the sinusoidal analysis, setting the proper parameters. A sinusoidal plus transient schema is obtained. Typical sound transformations have been implemented to validate the obtained results.

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In this work, a new estimator of the fundamental frequencies (F0 ) present in a polyphonic single-channel signal is developed. The signal is modeled in terms of a set of discrete partials obtained by the Complex Continuous Wavelet Transform (CCWT). The fundamental frequency estimation is based on the energy distribution of the detected partials of the input signal followed by an spectral smoothness technique. The proposed algorithm is designed to work with suppressed fundamentals, inharmonic partials and harmonic related sounds. The detailed technique has been tested over a set of input signals including polyphony 2 to 6, with high precision results that show the strength of the algorithm. The obtained results are very promising in order to include the developed algorithm as the basis of Blind Sound Source Separation or automatic score transcription techniques.

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