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 -