Modeling single-class data from multi-class data

Separating one set of data from a background of many sets

Software & Information Technology

This extensible technology isolates data (text, image, and voice) representing a target class from heterogeneous data representing multiple data categories of the same type. The method may be applied to identify speech from one speaker in audio containing several other speakers, and extends to language and gender identification and image and text applications. By auto-selecting data representing a particular class from multi-class data, nonessential artifacts may be removed from models trained on multi-class data, thereby enhancing detection and identification capabilities.

Potential applications include: speech applications (speaker/language recognition, gender identification), improved classification accuracy in pattern recognition, and biometric data discrimination.

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