Dezhong Peng

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This work proposes a novel dual-channel time-spread echo method for audio watermarking, aiming to improve robustness and perceptual quality. At the embedding stage, the host audio signal is divided into two subsignals, which are considered to be signals obtained from two virtual audio channels. The watermarks are implanted into the two subsignals(More)
Minor component analysis (MCA) is a powerful statistical tool for signal processing and data analysis. Convergence of MCA learning algorithms is an important issue in practical applications. In this paper, we will propose a simple MCA learning algorithm to extract minor component from input signals. Dynamics of the proposed MCA learning algorithm are(More)
Recently, Aissa-El-Bey et al. have proposed two subspace-based methods for underdetermined blind source separation (UBSS) in time-frequency (TF) domain. These methods allow multiple active sources at TF points so long as the number of active sources at any TF point is strictly less than the number of sensors, and the column vectors of the mixing matrix are(More)
It is known that the constant modulus (CM) property of the source signal can be exploited to blindly equalize time-invariant single-input–multiple-output (SIMO) and finite-impulse-response (FIR) channels. However, the time-invariance assumption about the channel cannot be satisfied in several practical applications, e.g., mobile communication. In this(More)
The convergence of minor-component analysis (MCA) algorithms is an important issue with bearing on the use of these methods in practical applications. This correspondence studies the convergence of Feng's MCA learning algorithm via a corresponding deterministic discrete-time (DDT) system. Some sufficient convergence conditions are obtained for Feng's MCA(More)
In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of(More)
The original Oja–Xu minor component analysis (MCA) learning algorithm is not convergent. This brief shows that by modifying Oja–Xu MCA learning algorithm with a normalization step the modified one could be convergent subject to some conditions satisfied. The convergence of the modified MCA learning algorithm is studied by analyzing the convergence of an(More)