DocumentCode :
1502627
Title :
Resolution Enhancement in \\Sigma \\Delta Learners for Superresolution Source Separation
Author :
Fazel, Amin ; Gore, Amit ; Chakrabartty, Shantanu
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Volume :
58
Issue :
3
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
1193
Lastpage :
1204
Abstract :
Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficient to overcome the artifacts due to large cross-channel redundancy, nonhomogeneous mixing, and high-dimensionality of the signal space. This paper proposes a novel framework that overcomes these limitations by integrating statistical learning directly with the signal measurement (analog-to-digital) process which enables high fidelity separation of linear instantaneous mixtures. At the core of the proposed approach is a min-max optimization of a regularized objective function that yields a sequence of quantized parameters which asymptotically tracks the statistics of the input signal. Experiments with synthetic and real recordings demonstrate significant and consistent performance improvements when the proposed approach is used as the analog-to-digital front-end to conventional source separation algorithms.
Keywords :
analogue-digital conversion; array signal processing; signal resolution; source separation; statistical analysis; ΣΔ modulation; analog-to-digital conversion; analog-to-digital process; cross channel redundancy; high-density sensor arrays; resolution enhancement; signal measurement; statistical learning; superresolution source separation; Analog-digital conversion; Delta-sigma modulation; Dynamic range; Image resolution; Robustness; Sensor arrays; Sensor phenomena and characterization; Signal resolution; Source separation; Statistical learning; $SigmaDelta$ modulation; Analog-to-information converters; high-density sensing; oversampling converters; source separation; superresolution;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2009.2034909
Filename :
5290030
Link To Document :
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