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This is achieved using a recursive least squares algorithm to model the secondary path and the primary noise with an autoregressive moving average model. The resulting model allows for predicting future values of the primary noise. Finally, the primary noise values predicted are filtered by a non-causal inverse of the secondary path model to generate the anti-noise signal. 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