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USINGSPARSEREPRESENTATIONSFOREXEMPLARBASEDCONTINUOUS DIGITRECOGNITION
– J. Gemmeke, L. Ten Bosch, L. Boves, B. Cranen
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unknown title
– Jort F. Gemmeke
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MISSING DATA IMPUTATION USING COMPRESSIVE SENSING TECHNIQUES FOR CONNECTED DIGIT RECOGNITION
– Jort Gemmeke, Bert Cranen
- 2009
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1
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SPARSE IMPUTATION FOR NOISE ROBUST SPEECH RECOGNITION USING SOFT MASKS
– J. F. Gemmeke, B. Cranen
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3
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Using sparse representations for missing data imputation in noise robust speech recognition
– J. F. Gemmeke, B. Cranen
- 2008
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Abstract
– Jort Gemmeke, Bert Cranen
- 901
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18
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Sparse Representation For Computer Vision and Pattern Recognition
– John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas Huang, Shuicheng Yan
- 2009
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Compressive sensing: a paradigm shift in signal processing
– Olga V. Holtz
- 812
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4
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Reaching over the gap: A review of efforts to link human and automatic speech recognition research
– Odette Scharenborg
- 2007
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Informing Multisource Decoding in Robust Automatic Speech Recognition
– Ning Ma
- 2008
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2
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Aysal, “Robust sampling and reconstruction methods for sparse signals in the precense of impulsive noise
– Rafael E. Carrillo, Student Member, Kenneth E. Barner, Senior Member, Tuncer C. Aysal
- 2009
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Real-Time Convex Optimization . . . -- Recent advances that make it easier to design and implement algorithms
– Jacob Mattingley, Stephen Boyd
- 2010
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4
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Noise robust digit recognition using sparse representations
– J. F. Gemmeke, B. Cranen
|
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3
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AN EFFICIENT METHOD FOR COMPRESSED SENSING
– Seung-jean Kim, Kwangmoo Koh, Michael Lustig, Stephen Boyd
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14
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Compressed sensing in astronomy
– J. Bobin, J-L. Starck, R. Ottensamer
|
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2
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Tensor Sparse Coding for Region Covariances
– Ravishankar Sivalingam, Daniel Boley, Vassilios Morellas
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Visual Tracking and Illumination . . .
– Xue Mei
- 2009
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3
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Morphological Diversity and Sparsity for Multichannel Data Restoration
– J. Bobin, Y. Moudden, J. Fadili, J.-L. Starck
- 2008
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Index Terms
– J. Bobin, J-l. Starck, R. Ottensamer
- 802
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