Speech Enhancement using Multiple Transducers

Abstract

In this thesis, three methods of speech enhancement techniques are investigated with applications in extreme noise environments. Various beamforming techniques are evaluated for their performance characteristics in terms of signal to (distant) noise ratio and tolerance to design imperfections. Two suitable designs are identified with contrasting performance characteristics — the second order differential array, with excellent noise rejection but poor robustness; and a least squares design, with adequate noise rejection and good robustness. Adaptive filters are introduced in the context of a simple noise canceller and later a post-processor for a dual beamformer system. Modifications to the least mean squares (LMS) filter are introduced to tolerate cross-talk between microphones or beamformer outputs. An adaptive filter based post-processor beamforming system is designed and evaluated using a simulation involving speech in noisy environments. The beamforming methods developed are combined with the modified LMS adaptive filter to further reduce noise (if possible) based on correlations between noise signals in a beamformer directed to the talker and a complementary beamformer (nullformer) directed away from the talker. This system shows small, but not insignificant, improvements in noise reduction over purely beamforming based methods. Blind source separation is introduced briefly as a potential future method for enhancing speech in noisy environments. The FastICA algorithm is evaluated on existing data sets and found to perform similarly to the post-processing system developed in this thesis. Future avenues of research in this field are highlighted