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Processing and analysis of sounds signals by Huang transform (Empirical Mode Decomposition: EMD)

Abstract : This dissertation explores the potential of EMD as analyzing tool for audio and speech processing. This signal expansion into IMFs is adaptive and without any prior assumptions (stationarity and linearity) on the signal to be analyzed. Salient properties of EMD such as dyadic filter bank structure, quasi-symmetry of IMF and fully description of IMF by its extrema, are exploited for denoising, coding and watermarking purposes. In speech signals denoising, we initially proposed a technique based on IMFs thresholding. A comparative analysis of performance of this technique compared to the denoising technique based on the wavelet. Then, to remedy the problem of the MMSE filters which requires an estimation of the spectral properties of noise, we introduced the ACWA filter in the denoising procedure. The proposed approach is consisted to filter all IMFs of the noisy signal by ACWA filter. This filtering approach is implemented in the time domain, and also applicable in the context of colored noise. Finally, to handle the case of hybrid speech frames, that is composed of voiced and unvoiced speech, we introduced a stationarity index in the denoising approach to detect the transition between the mixture of voiced and unvoiced sounds. In audio signals coding, we proposed four compression approaches. The first two approaches are based on the EMD, and the other two approaches exploit the EMD in association with Hilbert transform. In particular, we proposed to use a predictive coding of the instantaneous amplitude and frequency of the IMFs Finally, we studied the problem of audio signals watermarking in context of copyright protection. The number of IMFs can be variable depending on the attack type. The proposed approach involves inserting the mark in the extrema of last IMFs. In addition, we introduced a synchronization code in the procedure in order to facility the extraction of the mark. These contributions are illustrated on synthetic and real data and results compared to well established methods such as MMSE filter, wavelets approach, MP3 and AAC coders showing the good performances of EMD based signal processes. These findings demonstrate the real potential of EMD as analyzing tool (in adaptive way) in speech and audio processing.
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Kais Khaldi. Processing and analysis of sounds signals by Huang transform (Empirical Mode Decomposition: EMD). Signal and Image Processing. Télécom Bretagne, Université de Bretagne Occidentale, 2012. English. ⟨tel-00719637⟩

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