Alexander Bertrand (University of Leuven) ``Signal processing algorithms for wireless acoustic sensor networks``, Advisor: Prof. Marc Moonen (2011). This thesis contains several distributed adaptive signal or parameter estimation algorithms for wireless sensor networks (WSN). Several chapters in the thesis focus on (but are not limited to) the specific application of WSNs for audio acquisition, often referred to as wireless acoustic sensor networks (WASNs). We propose distributed techniques for signal enhancement, based on beamforming or other noise reduction techniques, with a minor focus on speech enhancement. The proposed algorithms are referred to as distributed adaptive node-specific signal estimation (DANSE) algorithms. The ‘node-specific’ aspect refers to the fact that the algorithms estimate different node-specific versions of a source signal, e.g., as it is locally observed by each sensor node. Although the main focus is on distributed adaptive signal estimation, the thesis also covers distributed parameter estimation techniques (such as distributed total least squares and bias-compensated RLS), sensor subset selection and voice activity detection in WASNs. For details, please access the full thesis or contact the author at alexander.bertrand@esat.kuleuven.be.