International audience—Nonlinear audio system identification generally relies on Gaussianity, whiteness and stationarity hypothesis on the input signal, although audio signals are non-Gaussian, highly correlated and non-stationary. However, since the physical behavior of nonlinear audio systems is input-dependent, they should be identified using natural audio signals (speech or music) as input, instead of artificial signals (sweeps or noise) as usually done. We propose an identification scheme that conditions audio signals to fit the desired properties for an efficient identification. The identification system consists in (1) a Gaussianization step that makes the signal near-Gaussian under a perceptual constraint; (2) a predictor filterbank that whitens the signal; (3) an orthonor-malization step that enhances the statistical properties of the input vector of the last step, under a Gaussianity hypothesis; (4) an adaptive nonlinear model. The proposed scheme enhances the convergence rate of the identification and reduces the steady state identification error, compared to other schemes, for example the classical adaptive nonlinear identification
In a telephone link, the voice timbre is impaired by spectral distortions generated by the analog parts of the link. We first evaluate from a perceptual point of view an equalization method consisting in matching the long term spectrum of the processed signal to a reference spectrum. This evaluation shows a satisfying restoration of the timbre for most speakers. For some speakers however, a noticeable spectral distortion remains. That is why we propose a multi-referenced equalizer, based on a classification of speakers and using a different reference spectrum for each class. This leads to a decrease of the spectral distortion and, as a consequence, to a significant improvement of the timbre correction.
In this paper, we propose the concept of "doping watermarking", whose principle is to add an imperceptible noise to an host signal in order to improve its properties. Especially, our aim is to reduce the spectral support of the probability density function (PDF) of an audio signal in order to match the conditions of the quantization theorem. In this context, we develop a specific audiowatermarking algorithm and test its performance on real audio signals. This watermark allows to recover the PDF of a digital signal from a sub-quantized version of the signal, with very low error.Index Terms-quantization theorem, sub-quantization, audiowatermarking, speech and audio processing.
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