The performance of adaptive acoustic echo cancelers (AEC) is sensitive to the nonstationarity and correlation of speech signals. In this paper, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the far-end speech. In the signal, we embed-in an extended conception of data hiding-an imperceptible white and stationary signal, i.e., a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely and takes advantage of its appropriate properties (stationary and white) to improve the robustness of the two-stage AEC to the nonstationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as adaptive watermark driven AEC (A-WdAEC), the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as maximum length sequences WdAEC (MLS-WdAEC), the watermark is built from MLS. Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/preprocessed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and undermodeling. Simulation results show the relevance of the “WdAEC” approach, compared to the classical “error-driven AEC.”

In this paper, we introduce a perceptual algorithm, called Perceptual Orthogonal Matching Pursuit (POMP), for efficient sparse modeling of wideband speech signals. As its name suggests, POMP is basically built upon the well-known Orthogonal Matching Pursuit (OMP) but differs from it in that it accounts for the human hearing properties. The perceptual component within POMP algorithm is represented by the perceptual weighting filter embedded into the distortion measure. Through a series of locally optimal updates, this measure is aimed at being minimized. POMP has the advantage of being able to handle any real dictionary, as opposed to state of the art perceptual algorithms conceived to handle sinusoidal dictionaries exclusively. We exemplify the application of the algorithm to sparse modeling of wideband speech signals using predefined and adaptive dictionaries. Simulation results show that POMP outperforms OMP and provides a significant improvement of atom selection over the considered dictionary.

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.

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 orthonormalization 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.

This paper presents a novel technique in spectrum sensing based on a new characterization of primary users signals in wideband communications. First, we have to remind that in cognitive radio networks, the very first task to be operated by a cognitive radio is sensing and identification of spectrum holes in the wireless environment. This paper summarizes the advances in the algebraic approach. Initial results have been already disseminated in few other conferences. This paper aims at finalizing and presenting the last results and the complete framework of the proposed technique based on algebraic spectrum discontinuities detection. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the algebraic framework is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the sensed subbands. In this context, a wideband spectrum sensing techniques was developed based on an analog decision function to multi-scale wavelet product. The proposed sensing techniques provide an effective sensing framework to identify and locate spectrum holes in the signal spectrum.

This paper presents a new structure of an Acoustic Echo Canceller (AEC) designed to operate in the Mobile Switching Center (MSC) of a GSM network. The purpose of such system is to cancel the echo for all the subscribers. Contrarily to the conventional AEC, the proposed combined AEC/CELP Predictor is able to take into account the non linearities introduced by the GSM speech coders/decoders. A short term predictor is used to model the behavior of the codecs. This new combined system presents higher performance compared to the conventional AEC.

In this paper, a new class of blind algorithms designed for decision feedback equalization of time varying channels, is proposed. We consider Markovian time variations of the impulse response of the channel as in radio mobile communications. The main idea is to modify classical blind algorithms (decision-directed, constant modulus algorithm,...) in order to give them self-adaptive knowledge of the channel non-stationarity. Simulations show that the proposed algorithms non-stationary DD and non-stationary CMA present better tracking capacity than the classical ones. Hence, they are able to improve the bit error rate especially for severe propagation conditions.

We propose a new adaptive algorithm designed to track system presented by a filter that has Markovian time evolution. As the non-stationary LMS (NSLMS) algorithm, the non-stationary RLS (NSRLS) algorithm performs better than the LMS and is able to identify the unknown order and parameters of the Markov model. However in the case of the NSRLS algorithm, the convergence speed of the Markovian parameter is very high compared to that of the NSLMS algorithm. Moreover, the NSRLS algorithm has a better tracking capacity than the NSLMS, especially when the filter poles that characterize time variations of the channel are close to the unit circle.