SANE2019 | Simon Doclo - Blind multi-microphone noise reduction and dereverberation algorithms

Simon Doclo, head of the Signal Processing Group at the University of Oldenburg, Germany, presents his work on blind multi-channel noise reduction and dereverberation at Columbia University, New York, NY, October 24, 2019.
More info on the SANE workshop series: http://www.saneworkshop.org/

Abstract: Despite the progress in speech enhancement algorithms, speech understanding in adverse acoustic environments with background noise, competing speakers and reverberation is still a major challenge in many speech communication applications. In this presentation, some recent advances in blind multi-microphone noise reduction and dereverberation algorithms will be presented, with a particular focus on the multi-channel Wiener filter (MWF). First, several methods to jointly estimate all required time-varying quantities, i.e. the relative transfer functions of the target speaker and the power spectral densities of the target speaker, the reverberation and the noise, will be presented. Second, speech enhancement algorithms for binaural hearing devices will be discussed, where the objective is not only to selectively extract the target speaker and suppress background noise and reverberation, but also to preserve the auditory impression of the complete acoustic scene. Aiming at preserving the binaural cues of all sound sources while not degrading the noise reduction performance, different extensions of the binaural MWF will be presented, both for diffuse noise as well as for interfering sources. Third, it will be shown how such algorithms can be used in acoustic sensor networks, exploiting the spatial distribution of the microphones. Evaluation results will be presented in terms of objective performance measures as well as subjective listening scores for speech intelligibility and spatial quality.