Parallel Processing of Large Scale Microphone Arrays for Sound Capture

Performance of microphone sound pick up is degraded by deleterious properties of the acoustic environment, such as multipath distortion (reverberation) and ambient noise. The degradation becomes more prominent in a teleconferencing environment in which the microphone is positioned far away from the speaker. Besides, the ideal teleconference should feel as easy and natural as face-to-face communication with another person. This suggests hands-free sound capture with no tether or encumbrance by hand-held or body-worn sound equipment. Microphone arrays for this application represent an appropriate approach. This research develops new microphone array and signal processing techniques for high quality hands-free sound capture in noisy, reverberant enclosures. The new techniques combine matched-filtering of individual sensors and parallel processing to provide acute spatial volume selectivity which is capable of mitigating the deleterious effects of noise interference and multipath distortion. The new method outperforms traditional delay-and-sum beamformers which provide only directional spatial selectivity. The research additionally explores truncated matched-filtering and random distribution of transducers to reduce complexity and improve sound capture quality. All designs are first established by computer simulation of array performance in reverberant enclosures. The simulation is achieved by a room model which can efficiently calculate the acoustic multipath in a rectangular enclosure up to a prescribed order of images. It also calculates the incident angle of the arriving signal. Experimental arrays were constructed and their performance was measured in real rooms. Real room data were collected in a hard-walled laboratory and a controllable variable acoustics enclosure of similar size, approximately 6 x 6 x 3 m. An extensive speech database was also collected in these two enclosures for future research on microphone arrays. The simulation results are shown to be consistent with the real room data. Localization of sound sources has been explored using cross-power spectrum time delay estimation and has been evaluated using real room data under slightly, moderately and highly reverberant conditions. To improve the accuracy and reliability of the source localization, an outlier detector that removes incorrect time delay estimation has been invented. To provide speaker selectivity for microphone array systems, a hands-free speaker identification system has been studied. A recently invented feature using selected spectrum information outperforms traditional recognition methods. Measured results demonstrate the capabilities of speaker selectivity from a matched-filtered array. In addition, simulation utilities, including matched -filtering processing of the array and hands-free speaker identification, have been implemented on the massively -parallel nCube super-computer. This parallel computation highlights the requirements for real-time processing of array signals.