Daniel V. Rabinkin

A DSP implementation of source location using microphone arrays.

The design, implementation, and performance of a low‐cost, real‐time DSP system for source location is discussed. The system consists of an eight‐element electret microphone array connected to a Signalogic DSP daughterboard hosted by a PC. The system determines the location of a speaker in the audience in an irregularly shaped auditorium. The auditorium presents a nonideal acoustical environment; some of the walls are acoustically treated but there still exist significant reverberation and a large amount of low‐frequency noise from fans in the ceiling. The source location algorithm is implemented in a two‐step process: The first step determines time delay of arrival (TDOA) for select microphone pairs. A modified version of the cross‐power spectrum phase method [M. Omologo and P. Svaizer, Proceedings of IEEE ICASSP 1994 (IEEE, New York, 1994), pp. II273–II276] is used to compute TDOAs and is implemented on the DSP daughterboard. The second step uses the computed TDOAs in a least‐mean‐square gradient descen...

Estimation of wavefront arrival delay for acoustical signals using the cross‐power spectrum phase technique

In order to provide spatially selective sound capture in a teleconferencing environment, microphone array systems require accurate determination of the location of the desired source. Sound source location in turn relies on estimating time delay of arrival (TDOA) of a sound wavefront across a given microphone sensor pair. The cross‐power spectrum phase (CPSP) method [M. Omologo and P. Svaizer, ICASSP 94] may be used for TDOA estimation. It is desirable to operate microphone array systems in untreated acoustical environments. Such an environment may produce multipath sound propagation and may contain moderate sources of interfering noise. The TDOA estimates computed using CPSP may become unreliable in such an environment. A strategy is presented to extract reliable TDOA information from the CPSP algorithm. The following measures are included: spectral weighting of the CPSP function based on knowledge of noise and sound source statistics, measuring the accuracy of the CPSP function by examining its shape, a...

DSP implementation of source location using microphone arrays

The design, implementation, and performance of a low-cost, real-time DSP system for source location is discussed. The system consists of an 8-element electret microphone array connected to a Signalogic DSP daughterboard hosted by a PC. The system determines the location of a speaker in the audience in an irregularly shaped auditorium. The auditorium presents a non-ideal acoustical environment; some of the walls are acoustically treated, but there still exists significant reverberation and a large amount of low frequency noise from fans in the ceiling. The source location algorithm is implemented in a two step process. The first step determines time delay of arrival (TDOA) for select microphone pairs. A modified version of the Cross- Power Spectrum Phase Method is used to compute TDOAs and is implemented on the DSP daughterboard. The second step uses the computed TDOAs in a least mean squares gradient descent search algorithm implemented on the PC to compute a location estimate.

Optimal truncation time for matched filter array processing

Matched filter array processing (MFA) has been shown to improve the signal-to-noise (SNR) quality for array speech capture in reverberant environments. However, under non-optimum conditions, MFA processing is computationally costly, and may produce little improvement or even subjective quality degradation as compared with simple time delay compensation (TDC). Appropriate truncation of the MFA filter bank is shown to reduce the computational burden without significantly reducing the capture SNR. This work attempts to find an optimal truncation time with respect to room size, wall absorption and the number of microphones used for the system. Simulations were conducted to evaluate MFA performance as a function of truncation length as these parameters were varied in situations typical of teleconferencing applications. It was demonstrated that judicious MFA truncation allows a reduction in computation load without sacrificing capture SNR.

Small‐scale matched filter array processing for spatially selective sound capture

Matched filter array (MFA) processing has a distinct advantage over delay‐sum beamforming for reverberant enclosures in that it is able to cohere significant reflected images in addition to the direct arrivals and is capable of spatial selectivity in three dimensions. However, previous study of this technique utilized a very large number of sensors, which is beyond the A/D capacity and computational power of current commercially available DSP hardware. In this paper, the results of using matched filtering with only eight sensors in real rooms are presented. It is found that a moderate amount of reverberation can be removed, which suggests that the technique may prove useful for high‐quality speech pickup for teleconferencing in a small‐room environment.

Optimum microphone placement for array sound capture

Microphone arrays can be used for high-quality sound pick up in reverberant and noisy environments. The beamforming capabilities of microphone array systems allow highly directional sound capture, providing superior signal-to-noise ratio (SNR) when compared to single microphone performance. There are two aspects in microphone array system performance: The ability of the system to locate and track sound sources, and its ability to selectively capture sound from those sources. Both aspects of system performance are strongly affected by the spatial placement of microphone sensors. A method is needed to optimize sensor placement based on geometry of the environment and assumed sound source behavior. The objective of the optimization is to obtain the greatest average system SNR using a specified number of sensors. A method is derived to evaluate array performance for a given array configuration defined by the above mentioned metrics. An overall performance function is described based on these metrics. A framework for optimum placement of sensors under the practical considerations of possible sensor placement and potential location of sound sources is also characterized.

Microphone array sensor placement optimization in reverberant environments

Microphone arrays can be used for high‐quality sound pickup in reverberant and noisy environments. The beamforming capabilities of microphone array systems allow highly directional sound capture, providing a superior signal‐to‐noise ratio (SNR) when compared to single microphone performance. Recent work [Rabinkin et al., ‘‘Optimum microphone placement for array sound capture,’’ Proc. SPIE 8/97] has addressed the issue of microphone placement for optimized array performance. A Monte Carlo procedure was described to evaluate average sound capture SNR for a given microphone configuration based on geometry‐related array performance statistics. Numerical optimization was performed for particular classes of sensor geometries based on the evaluated SNR. A uniformly distributed uncorrelated additive noise model was used to evaluate performance. An improved performance model is described which accounts for the correlated nature of additive noise generated by acoustic reflections of the source in a reverberant envi...

Signal processing for sound capture

High‐quality electret microphones and single‐chip processors are economical enough to be used in large numbers. This latitude opens opportunities for dynamic source location and sound capture with spatial selectivity in three dimensions. This report discusses algorithms for matched‐filter processing of microphone arrays and for coordinate tracking of moving talkers. A prototype conferencing system is demonstrated in which the automatic source locator steers both a video camera and a beam‐forming microphone array to capture image and audio from a moving talker. [Components of this research are supported by NSF Contract 397‐26740 and DARPA Contract DABT63‐93‐C0037, the New Jersey Commission on Science and Technology, and the corporate members of the Rutgers Center for Computer Aids for Industrial Productivity (CAIP).]