This study evaluates a spatial-filtering algorithm as a method to improve speech reception for cochlear-implant (CI) users in reverberant environments with multiple noise sources. The algorithm was designed to filter sounds using phase differences between two microphones situated 1 cm apart in a behind-the-ear hearing-aid capsule. Speech reception thresholds (SRTs) were measured using a Coordinate Response Measure for six CI users in 27 listening conditions including each combination of reverberation level (T60=0, 270, and 540 ms), number of noise sources (1, 4, and 11), and signal-processing algorithm (omnidirectional response, dipole-directional response, and spatial-filtering algorithm). Noise sources were time-reversed speech segments randomly drawn from the Institute of Electrical and Electronics Engineers sentence recordings. Target speech and noise sources were processed using a room simulation method allowing precise control over reverberation times and sound-source locations. The spatial-filtering algorithm was found to provide improvements in SRTs on the order of 6.5 to 11.0 dB across listening conditions compared with the omnidirectional response. This result indicates that such phase-based spatial filtering can improve speech reception for CI users even in highly reverberant conditions with multiple noise sources.
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http://dx.doi.org/10.1177/2331216514555489 | DOI Listing |
Ultrasound Med Biol
January 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Health Research, SINTEF, Trondheim, Norway.
Objective: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.
Methods: Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4515 McKinley Ave., St. Louis, MO, 63110, USA.
Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Laboratory of Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.
Pulsation noise in the piping system generated by the excitation of the pump source seriously affects the reliability of the pipeline system and mechanical equipment. The active noise control can effectively suppress the low-frequency noise in the liquid-filled pipeline. Active control methods with intrusive secondary sources generally use dynamic pressure sensors or hydrophones to collect signals, which destroy the structure of the pipe.
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