Kais Khaldi

Audio Watermarking Via EMD

In this paper a new adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark and the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving audio perceptual quality of the host signal. The data embedding rate of the proposed algorithm is 46.9-50.3 b/s. Relying on exhaustive simulations, we show the robustness of the hidden watermark for additive noise, MP3 compression, re-quantization, filtering, cropping and resampling. The comparison analysis shows that our method has better performance than watermarking schemes reported recently.

Speech Enhancement via EMD

In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.

Speech signal noise reduction by EMD

In this paper, a speech signal noise reduction based on a multiresolution approach referred to as Empirical Mode Decomposition (EMD) [1] is introduced. The proposed speech denoising method is a fully data-driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs), using a temporal decomposition called sifting process. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded using a shrinkage function. The denoising method is applied to speech with different noise levels and the results are compared to wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise.

A new EMD denoising approach dedicated to voiced speech signals

This paper introduces a new voiced speech denoising approach based on the empirical mode decomposition (EMD) associated to an appropriate sifting process. Noisy signal is decomposed adaptively into intrinsic oscillatory components called (IMFs). Since, the energy of a voiced speech signal is distributed over low and medium frequencies, the obtained lower order IMFs (high frequency components) are expected to be highly contaminated by noise. However, the last IMFs (low and medium frequency components) corresponding to the most structures of the signal, contain very low noise levels. Therefore the filtering of the low order IMFs may introduce a signal distortion rather than reducing noise. The principle of the proposed method is then based on filtering only the first IMFs considered very noisy by the adaptive center weighted average (ACWA) filter. A criterion based on the IMFss energies is used to select the very noisy IMFs that must be filtered. The denoising proposed method is applied successfully to voiced speech signal corrupted with additive white Gaussian noise. The reported results obtained for different noise levels, demonstrate the efficiency of the proposed method for reducing noise and its superiority over other denoising methods considered for comparison.

Voiced speech enhancement based on adaptive filtering of selected intrinsic mode functions

In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD–ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e. to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise.

Voiced/unvoiced speech classification-based adaptive filtering of decomposed empirical modes for speech enhancement

This study presents a speech filtering method exploiting the combined effects of the empirical mode decomposition (EMD) and the local statistics of the speech signal using the adaptive centre weighted average (ACWA) filter. The novelty lies in incorporating the frame class (voiced/unvoiced) in the conventional filtering using the EMD and the ACWA filter. The speech signal is segmented into frames and each one is broken down by the EMD into a finite number of intrinsic mode functions (IMFs). The number of filtered IMFs depends on whether the frame is voiced or unvoiced. An energy criterion is used to identify voiced frames while a stationarity index distinguishes between unvoiced and transient sequences. Reported results obtained on signals corrupted by additive noise (white, F16, factory) show that the proposed filtering in line with the frame class is very effective in removing noise components from noisy speech signal. Compared with filtering results of the wavelet, the ACWA, and the EMD-ACWA methods, the proposed technique gives much better results in terms of average segmental signal-to-noise ratio and listening quality based on perceptual evaluation speech quality score.

HHT-based audio coding

In this paper, a new audio coding scheme combining the Hilbert transform and the empirical mode decomposition (EMD) is introduced. Based on the EMD, the coding is fully a data-driven approach. Audio signal is first decomposed adaptively, by EMD, into intrinsic oscillatory components called intrinsic mode functions (IMFs). The key idea of this work is to code both instantaneous amplitude (IA) and instantaneous frequency (IF), of the extracted IMFs, calculated using Hilbert transform. Since IA (resp. IF) is strongly correlated, it is encoded via a linear prediction technique. The decoder recovers the original signal by superposition of the demodulated IMFs. The proposed approach is applied to audio signals, and the results are compared to those obtained by advanced audio coding (AAC) and MP3 codecs, and wavelets-based compression. Coding performances are evaluated using the bit rate, objective difference grade (ODG) and noise to mask ratio (NMR) measures. Based on the analyzed audio signals, overall, our coding scheme performs better than wavelet compression, AAC and MP3 codecs. Results also show that this new scheme has good coding performances without significant perceptual distortion, resulting in an ODG in range

Speech denoising by Adaptive Weighted Average filtering in the EMD framework

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