TY - JOUR AB - Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival. AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11927 IS - 5 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - acoustic signal processing KW - arbitrary transfer function KW - array signal processing KW - blind acoustic beamforming KW - direction-of-arrival KW - direction-of-arrival estimation KW - eigenvalues and eigenfunctions KW - generalized eigenvalue decomposition KW - gradient ascent adaptation algorithm KW - microphone arrays KW - microphones KW - narrowband array beamforming KW - sensor array KW - single-channel post-filter KW - spatially colored noise KW - transfer functions TI - Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition VL - 15 ER - TY - CONF AB - 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. AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11930 KW - acoustic filter-and-sum beamforming KW - acoustic room impulses KW - acoustic signal processing KW - adaptive principal component analysis KW - adaptive signal processing KW - architectural acoustics KW - constrained optimization problem KW - cross power spectral density KW - deterministic algorithm KW - deterministic algorithms KW - distant-talking environments KW - eigenvalues and eigenfunctions KW - eigenvector KW - enhanced signal KW - filter-and-sum beamformer KW - FIR filter coefficients KW - FIR filter coefficients KW - FIR filters KW - gradient methods KW - human-machine interfaces KW - iterative estimation KW - iterative methods KW - largest eigenvalue KW - microphone signals KW - multichannel signal processing KW - optimisation KW - principal component analysis KW - spectral analysis KW - stochastic gradient ascent algorithm KW - stochastic processes T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005) TI - Acoustic filter-and-sum beamforming by adaptive principal component analysis VL - 4 ER -