System and method for taxonomically distinguishing unconstrained signal data segments
First Claim
1. A system for taxonomically distinguishing grouped segments of signals captured in unconstrained manner by a transducer for a plurality of sources, the system comprising:
- (a) a vector construction unit constructing at least one vector of predetermined form for each of the grouped signal segments;
(b) a training unit coupled to said vector construction unit, said training unit including;
a decomposition portion executing an adaptive sparse transformation upon a joint corpus of vectors for a plurality of signal segments of distinct sources, said decomposition portion generating for each said vector in said joint corpus at least one adaptive decomposition defined on a sparse transformation plane as a coefficient weighted sum of a representative set of decomposition atoms, and,a discriminant reduction portion coupled to said decomposition portion, said discriminant reduction portion being executable to mutually associate decomposition atoms of the representative set in m-wise manner for determining a cooperative strength thereof in distinguishing one distinct source from another, within a multi-dimensional plane, and thereby derive from said representative set at least one optimal combination of atoms for cooperatively distinguishing signals attributable to different ones of the distinct sources, wherein m is greater than or equal to 2; and
,(c) a classification unit coupled to said vector construction unit, said classification unit including;
a projection portion projecting a spectral vector of an input signal segment onto said sparse transformation plane to generate an adaptive decomposition therefor as a coefficient weighted sum of said representative set of decomposition atoms, and,a classification decision portion coupled to said projection portion, said classification decision portion being executable to discover for said adaptive decomposition of said input signal segment a degree of similarity relative to each of the distinct sources according to the optimal combination, and to thereby determine one of the distinct sources to have generated the input signal segment according to the degree of similarity.
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Abstract
A system and method are provided for taxonomically distinguishing grouped segments of signal data captured in unconstrained manner for a plurality of sources. The system comprises a vector unit constructing for each of the grouped signal data segments at least one vector of predetermined form. A sparse decomposition unit selectively executes in at least a training system mode a simultaneous sparse approximation upon a joint corpus of vectors for a plurality of signal segments of distinct sources. The sparse decomposition unit adaptively generates at least one sparse decomposition for each vector with respect to a representative set of decomposition atoms. A discriminant reduction unit executes during the training system mode to derive an optimal combination of atoms from the representative set. A classification unit executes in a classification system mode to discover for an input signal segment a degree of correlation relative to each of the distinct sources.
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Citations
24 Claims
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1. A system for taxonomically distinguishing grouped segments of signals captured in unconstrained manner by a transducer for a plurality of sources, the system comprising:
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(a) a vector construction unit constructing at least one vector of predetermined form for each of the grouped signal segments; (b) a training unit coupled to said vector construction unit, said training unit including; a decomposition portion executing an adaptive sparse transformation upon a joint corpus of vectors for a plurality of signal segments of distinct sources, said decomposition portion generating for each said vector in said joint corpus at least one adaptive decomposition defined on a sparse transformation plane as a coefficient weighted sum of a representative set of decomposition atoms, and, a discriminant reduction portion coupled to said decomposition portion, said discriminant reduction portion being executable to mutually associate decomposition atoms of the representative set in m-wise manner for determining a cooperative strength thereof in distinguishing one distinct source from another, within a multi-dimensional plane, and thereby derive from said representative set at least one optimal combination of atoms for cooperatively distinguishing signals attributable to different ones of the distinct sources, wherein m is greater than or equal to 2; and
,(c) a classification unit coupled to said vector construction unit, said classification unit including; a projection portion projecting a spectral vector of an input signal segment onto said sparse transformation plane to generate an adaptive decomposition therefor as a coefficient weighted sum of said representative set of decomposition atoms, and, a classification decision portion coupled to said projection portion, said classification decision portion being executable to discover for said adaptive decomposition of said input signal segment a degree of similarity relative to each of the distinct sources according to the optimal combination, and to thereby determine one of the distinct sources to have generated the input signal segment according to the degree of similarity. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for taxonomically distinguishing grouped segments of signals captured in unconstrained manner by a transducer for a plurality of sources, the method comprising:
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constructing for each of the grouped signal segments at least one vector of predetermined form; selectively executing in a processor an adaptive sparse approximation to generate an adaptive decomposition of each said vector, said adaptive sparse approximation in a training system mode executing upon a joint corpus of vectors for a plurality of signal segments of distinct sources, generating at least one adaptive decomposition for each said vector defined on a sparse transformation plane as a coefficient weighted sum of a representative set of decomposition atoms; executing discriminant reduction in a processor during the training system mode to mutually associate decomposition atoms of the representative set in m-wise manner for determining a cooperative strength thereof in distinguishing one distinct source from another, within a multi-dimensional subspace, and thereby derive from said representative set at least one optimal combination of atoms for cooperatively distinguishing signals attributable to different ones of the distinct sources, wherein m is greater than or equal to 2; projecting a spectral vector of an input signal segment onto said sparse transformation plane to generate an adaptive decomposition therefor as a coefficient weighted sum of said representative set of decomposition atoms; and
,executing classification upon said adaptive decomposition of an input signal segment during a classification system mode, said classification including executing a processor to discover a degree of similarity for said input signal segment relative to each of the distinct sources according to the optimal combination, and to thereby determine one of the distinct sources to have generated the input signal segment according to the degree of similarity. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification