Institution
New England Conservatory of Music
Education•Boston, Massachusetts, United States•
About: New England Conservatory of Music is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Music education & Disturbance (ecology). The organization has 27 authors who have published 47 publications receiving 1360 citations.
Topics: Music education, Disturbance (ecology), Singing, Land use, Musical
Papers
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TL;DR: Findings are that both direct correction and simple underlining of errors are significantly superior to describing the type of error, even with underlining, for reducing long-term error.
1,036 citations
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107 citations
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01 Jan 2005TL;DR: The experimental results confirm that the proposed method suppresses noise (CMOS/CCD image sensor noise model) while effectively interpolating the missing pixel components, demonstrating a significant improvement in image quality when compared to treating demosaicing and denoising problems independently.
Abstract: The output image of a digital camera is subject to a severe degradation due to noise in the image sensor. This paper proposes a novel technique to combine demosaicing and denoising procedures systematically into a single operation by exploiting their obvious similarities. We first design a filter as if we are optimally estimating a pixel value from a noisy single-color image. With additional constraints, we show that the same filter coefficients are appropriate for CFA interpolation (demosaicing) given noisy sensor data. The proposed technique can combine many existing denoising algorithms with the demosaicing operation. In this paper, a total least squares denoising method is used to demonstrate the concept. The algorithm is tested on color images with pseudo-random noise and on raw sensor data from a real CMOS digital camera that we calibrated. The experimental results confirm that the proposed method suppresses noise (CMOS image sensor noise model) while effectively interpolating the missing pixel components, demonstrating a significant improvement in image quality when compared to treating demosaicing and denoising problems independently.
50 citations
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18 Mar 2005TL;DR: A method is developed to reduce the contribution from the irrelevant image patches, which will sharpen the edges and reduce edge artifacts at the same time, which demonstrates the effectiveness of the TLS algorithms.
Abstract: In this paper, we present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed noise. An image patch from an ideal image is modeled as a linear combination of image patches from the noisy image. We propose to fit this image model to the real-world image data in the total least square (TLS) sense, because the TLS formulation allows us to take into account the uncertainties in the measured data. We develop a method to reduce the contribution from the irrelevant image patches, which will sharpen the edges and reduce edge artifacts at the same time. Although the proposed algorithm is computationally demanding, the image quality of the output image demonstrates the effectiveness of the TLS algorithms.
35 citations
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Massachusetts Eye and Ear Infirmary1, Medical College of Wisconsin2, Auburn University3, New England Conservatory of Music4, University of Southern California5, Loma Linda University Medical Center6, Shenandoah University7, Cleveland Clinic8, University of Texas at San Antonio9, Columbia University Medical Center10, Princeton University11, Drexel University12, University of Illinois at Urbana–Champaign13, University of Utah14, Brigham and Women's Hospital15
TL;DR: In this paper, the authors review the information that exists relating to singing and COVID-19 and provide guidance based on what we know: the best available data, analyzed and scrutinized by a panel of experts in the medical, behavioral and basic science world of voice care, of whom many are professional singers, choir directors or teachers of singing.
35 citations
Authors
Showing all 27 results
Name | H-index | Papers | Citations |
---|---|---|---|
Keigo Hirakawa | 24 | 107 | 2347 |
Matthew J. Duveneck | 16 | 39 | 931 |
Jean Chandler | 3 | 4 | 1081 |
Jill Gatlin | 3 | 4 | 22 |
Hermann Hudde | 2 | 6 | 9 |
Rebecca Cypess | 2 | 3 | 10 |
Lawrence Scripp | 2 | 4 | 24 |
Richard Colwell | 1 | 1 | 19 |
Joshua Gilbert | 1 | 1 | 5 |
Angela Beeching | 1 | 1 | 2 |
Roger C. Graybill | 1 | 2 | 1 |
Leah McKinnon-Howe | 1 | 1 | 3 |
Katherine Balch | 1 | 1 | 3 |
Jonathan Andrew Govias | 1 | 1 | 2 |
Kay Dunlap | 1 | 1 | 3 |