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Full-Text Articles in Engineering
Double-Talk Robust Fast Converging Algorithms For Network Echo Cancellation, T. Gansler, Steven L. Grant, J. Benesty, M. M. Sondhi
Double-Talk Robust Fast Converging Algorithms For Network Echo Cancellation, T. Gansler, Steven L. Grant, J. Benesty, M. M. Sondhi
Electrical and Computer Engineering Faculty Research & Creative Works
Echo cancelers which cover longer impulse responses (greater than or equal to 64 ms) are desirable. Long responses create a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers devised by the ITU (International Telecommunication Union). In general, faster convergence implies a higher sensitivity to near-end disturbances, especially "double-talk". Recently, a fast converging algorithm called proportionate NLMS (normalized least mean squares) algorithm (PNLMS) has been proposed. This algorithm exploits the sparseness of the echo path. In this paper we propose a method for making the PNLMS algorithm more robust against double-talk. The slower …
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Electrical and Computer Engineering Faculty Research & Creative Works
Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed …