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This paper presents a real-time architecture of an improved single-channel speech enhancement system based on phase-aware multi-band complex spectral subtraction. Using the proposed technique, the short-time spectral magnitude of the clean speech signal is estimated by considering the spectral phase of the speech and noise signal components. Moreover, the estimated spectral phase of the clean speech signal is also utilized for signal reconstruction in the time domain. The proposed system is made of the basic preprocessing module followed by an short-time Fourier transform analyzer, a noise power estimator based on improved minima controlled recursive array, a phase estimator unit and an overlap-add synthesis unit. The proposed architecture is implemented on a Field Programmable Gate Array (FPGA) using the Xilinx ISE tool. The overall resource utilization and the maximum operating frequency are also computed for a Virtex-6 FPGA chip. It has been experimentally shown that the proposed speech enhancement framework performs better than the other existing standard benchmark methods in terms of various quality and intelligibility assessment metrics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel phase estimator based on fully-traversed Discrete Fourier Transform (DFT) which takes all possible truncated DFT spectra into account such that it possesses two merits of `direct phase extraction’ (namely accurate instantaneous phase information can be extracted without any correction) and suppressing spectral leakage. This paper also proves that the proposed phase estimator complies with the 2-parameter joint estimation model rather than the conventional 3-parameter joint model. Numerical results verify the above two merits and demonstrate that the proposed estimator can extract phase information from noisy multi-tone signals. Finally, real data analysis shows that fully-traversed DFT can achieve a better classification on the phase of steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) than the conventional DFT estimator does. Besides, the proposed phase estimator imposes no restrictions on the relationship between the sampling rates and the stimulus frequencies, thus it is capable of wider applications in phase-coded SSVEP BCIs, when compared with the existing estimators.

A novel Viterbi-type adaptive maximum likelihood sequence detection algorithm is developed and investigated in coherent optical communication systems in the presence of both laser phase noise and nonlinear phase noise. Compared with the nonadaptive version, it can self-adjust the effective memory length of the phase estimator and automatically deliver the optimum bit-error rate performance without requiring any prior statistical knowledge of the optical channel. The phase error variance of the estimator is also analytically investigated. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel phase estimator based on fully-traversed Discrete Fourier Transform (DFT) which takes all possible truncated DFT spectra into account such that it possesses two merits of 'direct phase extraction' (namely accurate instantaneous phase information can be extracted without any correction) and suppressing spectral leakage. This paper also proves that the proposed phase estimator complies with the 2-parameter joint estimation model rather than the conventional 3-parameter joint model. Numerical results verify the above two merits and demonstrate that the proposed estimator can extract phase information from noisy multi-tone signals. Finally, real data analysis shows that fully-traversed DFT can achieve a better classification on the phase of steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) than the conventional DFT estimator does. Besides, the proposed phase estimator imposes no restrictions on the relationship between the sampling rates and the stimulus frequencies, thus it is capable of wider applications in phase-coded SSVEP BCIs, when compared with the existing estimators. [ABSTRACT FROM AUTHOR]