The objective of this paper is to show the ability of complex bandpass filterbanks to extract the intermodulation information that appears when two audio signals interact inside the same analysis band. To perform the analysis a sinusoidal model of the signals has been assumed. Three kinds of signals have been analyzed: a sum of two cosines, a sum of two linear chirps and a sum of two exponential chirps. The complex bandpass filtering of the signals is carried out using a new algorithm based on the Complex Continuous Wavelet Transform. The developed algorithm has been validated comparing the practical results with the theoretical instantaneous amplitude and instantaneous phase of the obtained model of the signals. With the appropriate width, the complex bandpass filters show the same behaviour as our perceptual ability to discriminate interacting tones when they fall inside a critical band of the human ear.

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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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