Title :
A neural network approach to the detection problem using joint time-frequency distributions
Author :
Malkoff, Donald ; Cohen, Leon
Author_Institution :
General Electric Aerospace, Moorstown, NJ, USA
Abstract :
A neural network algorithm for the detection and classification of transients in noise is described. The inputs are time-frequency representations modified so their entry location into the network of nodes is independent of the starting time of the transient. The algorithm is three-layered and feedforward, and is capable of training or testing in a single pass
Keywords :
computerised signal processing; digital simulation; neural nets; random noise; signal detection; transients; classification of transients; feedforward algorithm; joint time-frequency distributions; neural network; noise; protocol; single pass testing; single pass training; three layered algorithm; time-frequency representations; Educational institutions; Feedforward systems; Neural networks; Noise level; Parallel processing; Radar; Sonar applications; Spectrogram; Testing; Time frequency analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.116192
