A neural net application to signal identification

Results from an ongoing research project concerning detection and identification of signals in a relatively quiescent background are discussed. The signals to be identified are of varying duration, and the number of different signal classes is large (> 1,000) and is frequently expanded or reduced. The author's approach to solving this real-world problem is presented, and initial results are shown.<
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