Speech enhancement with a new generalized eigenvector blocking matrix for application in a generalized sidelobe canceller - Warsitz, Ernst; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008))
The generalized sidelobe canceller by Griffith and Jim is a robust beamforming method to enhance a desired (speech) signal in the presence of stationary noise. Its performance depends to a high degree on the construction of the blocking matrix which produces noise reference signals for the subsequent adaptive interference canceller. Especially in reverberated environments the beamformer may suffer from signal leakage and reduced noise suppression. In this paper a new blocking matrix is proposed. It is based on a generalized eigenvalue problem whose solution provides an indirect estimation of the transfer functions from the source to the sensors. The quality of the new generalized eigenvector blocking matrix is studied in simulated rooms with different reverberation times and is compared to alternatives proposed in the literature.
Keywords: adaptive interference canceller; adaptive signal processing; array signal processing; beamforming method; eigenvalues and eigenfunctions; generalized eigenvector blocking matrix; generalized sidelobe canceller; interference suppression; matrix algebra; noise suppression; speech enhancement; transfer function estimation; transfer functions

Acoustic filter-and-sum beamforming by adaptive principal component analysis - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005))
For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.
Keywords: acoustic filter-and-sum beamforming; acoustic room impulses; acoustic signal processing; adaptive principal component analysis; adaptive signal processing; architectural acoustics; constrained optimization problem; cross power spectral density; deterministic algorithm; deterministic algorithms; distant-talking environments; eigenvalues and eigenfunctions; eigenvector; enhanced signal; filter-and-sum beamformer; FIR filter coefficients; FIR filter coefficients; FIR filters; gradient methods; human-machine interfaces; iterative estimation; iterative methods; largest eigenvalue; microphone signals; multichannel signal processing; optimisation; principal component analysis; spectral analysis; stochastic gradient ascent algorithm; stochastic processes