We present a method for smart auscultation by proposing a novel blind recovery of the original cardiac and respiratory sounds from a single observation mixture, in the framework of nonnegative matrix factorization (NMF). The method learns the basis spectra of the mixing sources in unsupervised or semisupervised fashion depending upon the applications. A modified NMF technique is proposed, which enforces the spectral structure of the target sources in mixture factorization, resulting in good separation of target sources, even in the presence of nonstationary noise. Moreover, data is processed in small batches which 1) enables dynamic bases spectra update technique to mitigate the spectral variations of the mixing sources, and 2) reduces computational complexity. The analytical work is verified through simulations using synthetic as well as actual clinical data collected from different subjects in different clinical sittings. The proposed smart auscultation method demonstrates excellent results even in noisy clinical environments.

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Listening to cardiac and respiratory sounds called as auscultation is a non-invasive medical procedure, which provides useful information about the behavior of the heart and the lung. Cardiac and respiratory sounds interfere with each other as well as with other sounds like snore, speech or traffic noise, which compromises the effectiveness of auscultation. This paper addresses the problem of auscultation in complex auditory environments, inspired by the coincidence detection model which suggests sound localization via estimating interaural level difference and interaural time difference. The proposed method, exploits the sparsity of cardiac and respiratory sounds and makes use of a degenerate unmixing estimation technique (DUET), which uses only two observations to recover an arbitrary number of sources, which suits well in scenarios where the number of sources can vary. The DUET approach uses timefrequency analysis to produce a two dimensional histogram of attenuation-delay estimates, where peaks in the histogram indicate the sources in a mixture. A mask is computed using attenuation-delay mixing parameters to recover the original sources. It is shown that excellent time-frequency masks exist for cardiac and respiratory sounds. The performance of the proposed method is demonstrated through a series of experiments using real data, exhibiting superior source recovery than previous techniques.

Due to budget constraints, many multibeam sonar customers are being required to operate smaller ocean survey vessels, equipped with less-expensive oceanographic tools. At the same time, ocean survey mission requirements are becoming more and more demanding.In response to this dilemma, L-3 SeaBeam Instruments, Inc. of East Walpole, MA has developed an innovative method for mathematically extending the performance of smaller, low-cost multibeam systems. This enhanced signal processing technique uses Array Extrapolation (AE) to provide:-Narrower receive beam dimensions -Better separation of closely spaced targets -improved signal-to-noise performance This paper studies the effectiveness of this technique as employed in an actual SEA BEAM 2112 multibeam sonar system by comparing several bathymetric data sets, acquired during survey, with and without AE being implemented. After processing the data, an examination is made of the beampatterns, the standard deviations obtained after detrending the data, and the maps produced from the bathymetric data. In conclusion, it is found that the AE technique does improve the results in most situations encountered in practice, and does not degrade sonar performance in the remaining situations.