Large-aperture microphone arrays have the potential for providing quality speech acquisition from multiple talkers over a large focal area for such applications as teleconferencing and speech-recognition input. The cost of computing is rapidly approaching the point at which these arrays will be practical in many common environments. However, an important issue is the calibration of an array. We discuss procedures to accurately determine both the coordinates in three-dimensions for the position of each microphone and the individual gains of each microphone/microphone channel. For the positions, we found that calibration by direct measurement, using a surveyor's transit, is simply too error prone, time consuming, and difficult for large arrays and is too inaccurate for small arrays. We have also seen that without the careful matching of inexpensive electret microphones, the channel sensitivities vary by as much as 6 dB and performance can be enhanced by suitable compensation. This paper describes new apparatus and techniques for automatic calibration using acoustic signals. Experimental results using both small and large-aperture microphone array systems show the acoustically-determined positions to be more consistent than the ones measured directly. Gain measurements are somewhat more difficult but gains can be found within 1-2 dB with reasonable care.

A large array of microphones is being studied as a possible means of acquiring data in offices, conference rooms, and auditoria without requiring close-talking microphones. An array that surrounds all possible sources has a large aperture and such arrays have attractive properties for accurate spatial resolution and significant signal-to-noise enhancement. For the first time, this paper presents all the details of a real-time, source-location algorithm (LEMSalg) based on time-of-arrival delays derived from a phase transform applied to the generalized cross-power spectrum. It is being used successfully in a representative environment where microphone SNRs are below 0 dB. We have found that many small features are required to make a useful location estimating algorithm work and work well in real-time. We present an experimental evaluation of the current algorithm's performance using data taken with the Huge Microphone Array (HMA) system, which has 448 microphones in a noisy, reverberant environment. Using off-line computation, we also compared the LEMSalg to two alternative methods. The first of these adds local beamforming to the preprocessing of the base algorithm, increasing performance significantly at modest additional computational cost. The second algorithm maximizes the total steered-response power in the same phase transform. While able to derive good position estimates from shorter data runs, this method is two orders of magnitude more computationally expensive and is not yet suitable for real-time use.

Large-aperture microphone arrays have the potential for providing quality speech acquisition from multiple talkers over a large focal area for such applications as teleconferencing and speech-recognition input. The cost of supporting computing technology is quickly approaching the point in which these arrays will be practical in many common environments. However, an important issue is the calibration of an array that has been instal1ed in some medium-sized to large room. By calibration, we mean the determination of the precise set of coordinates in three-dimensions for the positions of the microphones. Calibration by direct measurement or even by a 'laser transit is simply too error-prone, time-consuming and painful. This paper describes new apparatus and techniques for automatic calibration using acoustic signals. Experimental results using our large-aperture microphone array system show the automatical1y-determined positions to be even more consistent than the ones measured directly. The paradigm for using the apparatus and technique most advantageously is also presented.

Microphone arrays offer the potential of obtaining a high-quality speech signal from a remote talker in a noisy, multi-source environment. In many important applications, sources of interest may be located anywhere within a large focal area and thus it is desirable that an array's performance be uniform over that area. In this paper, simulated and measured results are presented indicating the performances of representative small and large aperture arrays at various points in a large focal area. From these results, it is clear that uniformity over a large focal area is an advantage of large-aperture arrays.

Poor sound pick up by remote microphones in multimedia applications, conference rooms and auditoria has traditionally hampered speech recognition and communication among spatially-separated groups. The problems are reverberation, acoustic noise, and the variability of the radiation pattern of unconstrained talkers. One potential solution that is becoming increasingly practical is to use an array of microphones and sophisticated signal processing. In this paper a brief description of a large, real-time, working system is presented and its measured beamforming performance is compared to what is predicted from a mathematical model. A combination of a synchronized test signal/system and a careful mathematical model results in the performances matching surprisingly well. From this match of theory to practice, we are able to draw some important inferences about future system improvements.

The use of arrays of microphones having a large number of elements (hundreds) is now in place in research laboratories, and will soon be practical for real applications. In rooms of auditorium or conference size, a large number of microphones will virtually always imply that the aperture will be large compared to the focal distance. This requires understanding the volume selectivity of irregular, widely-distributed sets of microphones. In this paper, several pictures of the beamforming performance of large-aperture distributions of microphones are presented. The purpose is to illustrate some of the pitfalls in large-aperture microphone-array design.

The Huge Microphone Array (HMA) project started in February 1994 to design, construct, and test a real-time 512-microphone array system and to develop algorithms for use on it. Analysis of known algorithms showed that signal-processing performance of over 6 Gigaflops would be required; at the same time, there was a need for "portability", i.e., fitting into a small van. These tradeoffs and many others have led to a unique design in both hardware and software. This paper presents the design and its justifications. Performance data for a few important algorithms relative to usage of processing-capability, response latency, and difficulty of programming are discussed.

This paper provides a capsular introduction to the theoretical framework and experimental applications of the Polar Exponential Grid (PEG) transformation, in the context of image analysis. The PEG transformation is an isomorphic (1) representation of the image intensity array that simplifies, and potentially offers new insights about, a variety of tasks in computational vision. We describe the PEG transform representation; we briefly survey its functional precursors in optical computing and image processing. We then give an example of PEG-based image analysis for rotation-and-scale variant template matching and, present the PEG transform as a motif for a class of problems in stochastic estimation of object boundaries.