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This paper deals with the auxiliary noise-based methods for active noise control (ANC) systems with online secondary path modeling (SPM). The proposed method comprises two adaptive filters: the modified Filtered-X normalized least-mean-square algorithm-based ANC filter, and the normalized least-mean-square algorithm-based SPM filter excited by auxiliary noise. The auxiliary noise injected for online SPM, degrades the noise-reduction performance of the ANC system. A two-stage gain scheduling strategy is proposed to vary power of the auxiliary noise. In the first stage the gain is varied on the basis of power of the error signal of SPM filter, and in the second stage the gain is varied on the basis of the correlation estimate of the two adjacent samples of the error signal of SPM filter. The main idea is to inject large-power auxiliary noise at the start up or when a change in the acoustic paths is detected, and to reduce the power as the system converges. The proposed method achieves a fast convergence of the SPM filter and gives a robust performance in the presence of strong perturbation in acoustic paths. Furthermore, the proposed method improves the noise-reduction performance at steady-state even in the presence of an uncorrelated disturbance at the error microphone. Moreover, the improved performance is achieved at a lower computational cost as compared with a recent method proposed in [A. Carini, and S. Malatini, “Optimal variable step-size NLMS algorithms with auxiliary noise power scheduling for feedforward active noise control,”IEEE Trans. Audio, Speech Lang. Process., vol. 16, no. 8, pp. 1383-1395, Nov. 2008]. Extensive simulations are carried out to verify the effectiveness of the proposed method.