Hydrodynamic imaging using the lateral line plays a critical role in fish behavior. To engineer such a biologically inspired sensing system, we developed an artificial lateral line using MEMS (microelectromechanical system) technology and explored its localization capability. Arrays of biomimetic neuromasts constituted an artificial lateral line wrapped around a cylinder. A beamforming algorithm further enabled the artificial lateral line to image real-world hydrodynamic events in a 3D domain. We demonstrate that the artificial lateral line system can accurately localize an artificial dipole source and a natural tail-flicking crayfish under various conditions. The artificial lateral line provides a new sense to man-made underwater vehicles and marine robots so that they can sense like fish.
Direction finding of more sources than sensors is appealing in situations with small sensor arrays. Potential applications include surveillance, teleconferencing, and auditory scene analysis for hearing aids. A new technique for time-frequency-sparse sources, such as speech and vehicle sounds, uses a coherence test to identify low-rank time-frequency bins. These low-rank bins are processed in one of two ways: (1) narrowband spatial spectrum estimation at each bin followed by summation of directional spectra across time and frequency or (2) clustering low-rank covariance matrices, averaging covariance matrices within clusters, and narrowband spatial spectrum estimation of each cluster. Experimental results with omnidirectional microphones and colocated directional microphones demonstrate the algorithm's ability to localize 3-5 simultaneous speech sources over 4 s with 2-3 microphones to less than 1 degree of error, and the ability to localize simultaneously two moving military vehicles and small arms gunfire.
Extraction of a target sound source amidst multiple interfering sound sources is difficult when there are fewer sensors than sources, as is the case for human listeners in the classic cocktail-party situation. This study compares the signal extraction performance of five algorithms using recordings of speech sources made with three different two-microphone arrays in three rooms of varying reverberation time. Test signals, consisting of two to five speech sources, were constructed for each room and array. The signals were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio of the output. A frequency-domain minimum-variance distortionless-response beamformer outperformed the time-domain based Frost beamformer and generalized sidelobe canceler for all tests with two or more interfering sound sources, and performed comparably or better than the time-domain algorithms for tests with one interfering sound source. The frequency-domain minimum-variance algorithm offered performance comparable to that of the Peissig-Kollmeier binaural frequency-domain algorithm, but with much less distortion of the target signal. Comparisons were also made to a simple beamformer. In addition, computer simulations illustrate that, when processing speech signals, the chosen implementation of the frequency-domain minimum-variance technique adapts more quickly and accurately than time-domain techniques.
For some time, compact acoustic vector sensors (AVSs) capable of sensing particle velocity in three orthogonal directions have been used in underwater acoustic sensing applications. Potential advantages of using AVSs in air include substantial noise reduction with a very small aperture and few channels. For this study, a four-microphone array approximating a small (1 cm3) AVS in air was constructed using three gradient microphones and one omnidirectional microphone. This study evaluates the signal extraction performance of one nonadaptive and four adaptive beamforming algorithms. Test signals, consisting of two to five speech sources, were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio (SNR) of the output. For a three-microphone array, robust and nonrobust versions of a frequency-domain minimum-variance (FMV) distortionless-response beamformer produced SNR improvements of 11 to 14 dB, and a generalized sidelobe canceller (GSC) produced improvements of 5.5 to 8.5 dB. In comparison, a two-microphone omnidirectional array with a spacing of 15 cm yielded slightly lower SNR improvements for similar multi-interferer speech signals.
A collocated microphone array, including three gradient microphones with different orientations and one omnidirectional microphone, was used to acquire data in a sound-treated room and in an outdoor environment. This arrangement of gradient microphones represents an acoustic vector sensor used in air. Beamforming techniques traditionally associated with much larger uniformly spaced arrays of omnidirectional sensors are extended to this compact array (1 cm3) with encouraging results. A frequency–domain minimum-variance beamformer was developed to work with this array. After a calibration of the array, the recovery of sources from any direction is achieved with high fidelity, even in the presence of multiple interferers. SNR gains of 5–12 dB with up to four speech sources were obtained with both indoor and outdoor recordings. This algorithm has been developed for new MEMS-type microphones that further reduce the size of the sensor array.
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