An active noise canceller based on neural identifier is presented. The noise signal inside a car is identified by multi-layer neural network. At each time step the noise signal at the next time step is predicted by the identifier network, and compensated accordingly. Since the noise signal shows very complex characteristics, we first classify the noise into 7 different classes and train the identifier network separately. The developed neural noise canceller demonstrates much better performance than popular filtered-X LMS algorithm.
