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Adaptive notch filters (ANFs) are known to have convergence problems due to their non-quadratic error surface. We propose two approaches to improve the convergence of the ANF. The first approach is based on the method of stochastic search. The second approach checks to see whether the estimated signal is correlated to the measurement or is just filtered white noise. The ANF is reinitialized when the estimated signal is filtered white noise (i.e. when the ANF misses the right frequency). Both of these methods show superior convergence comparing to the classical Nehorai ANF.