Index of content:
Volume 123, Issue 4, April 2008
- ACOUSTIC SIGNAL PROCESSING 
123(2008); http://dx.doi.org/10.1121/1.2871764View Description Hide Description
This paper describes an acoustical array combining microphones and piezoelectric devices. Conventional microphone arrays have been widely utilized to realize noise reduction, sound separation and direction of arrival estimation system. However, when a conventional microphone array is mounted on a real system, such as a machine, vehicle or robot, the microphones are set extremely close to the system’s actual body. In such cases, the noise from the system itself, such as motors, gears, and engines, namely internal noise, often becomes a troublesome problem. It is difficult to reduce internal noise utilizing a conventional microphone array because internal noise sources are extremely close to the microphones. As internal noise is not always stationary, statistically independent or sparse, most useful blind source separation approaches, such as independent component analysis and the sparseness approach, cannot be employed. Our aim is to reduce internal noise utilizing microphones and piezoelectric devices attached to the internal noise source. In this paper, a general description of the acoustical array is formulated and the characteristic features of microphones and piezoelectric devices in an acoustical array are given. An acoustical array combining microphones and piezoelectric devices is also described with some experimental results.
123(2008); http://dx.doi.org/10.1121/1.2885746View Description Hide Description
The work addresses the definition of a wavelet that is adapted to analyze a flexural impulse response of a beam or plate that can be modeled with the Euler–Bernoulli bending theory. The wavelet gives the opportunity to directly analyze the dispersion characteristics of a pulse. The aim is to localize a source or to measure material parameters. An overview of the mathematical properties of the wavelet is presented. An algorithm for the optimal extraction of the dispersion characteristics with the use of genetic algorithms is outlined. The application of the wavelet is shown in an example and experiment.
123(2008); http://dx.doi.org/10.1121/1.2871597View Description Hide Description
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 speechsources over 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.
Angle-dependent ultrasonic detection and imaging of brachytherapy seeds using singular spectrum analysis123(2008); http://dx.doi.org/10.1121/1.2875740View Description Hide Description
Transrectal-ultrasound-guided brachytherapy uses small titanium-shelled radioactive seeds to locally treat prostate cancer. During the implantation procedure, needles inserted transperitoneally cause gland movement resulting in seed misplacement and suboptimal dosimetry. In a previous study, an algorithm based on singular spectrum analysis (SSA) applied to envelope-detected ultrasound signals was proposed to determine seed locations [J. Mamou and E. J. Feleppa, J. Acoust. Soc. Am.121, 1790–1801 (2007)]. Successful implementation of the SSA algorithm could allow correcting dosimetry errors during the implantation procedure. The algorithm demonstrated promise when the seed orientation was parallel to the needle and normal to the ultrasound beam. In this present study, the algorithm was tested when the seed orientation deviated up to 22° from normality. Experimental data from a seed in an ideal environment and in beef were collected with a single-element, spherically focused, transducer. Simulations were designed and evaluated with the algorithm. Finally, objective quantitative scoring metrics were developed to evaluate the algorithm performance and for comparison with -mode images. The results quantitatively established that the SSA algorithm always outperformed -mode images and that seeds could be detected accurately up to a deviation of approximately 10°.