Abstract: In this study, a transform domain based adaptive noise cancellation algorithm is proposed to enhance noise carrying speech signals. The algorithm deals with situations where the microphones should locate in close proximity, as they cancel out the crosstalk effects. In other words, the source of the noise signal is not available separately and is independent of the desired speech signal. This is the case in mobile phones and hands-free systems, where the smallness of the dimension of the applied speech enhancement system is desirable. In the proposed algorithm the Discrete Sine Transform (DST) is used as self orthogonalizing transform to address the eigen-spread problem of adaptive filter, whereas Least Mean Mixed-Norm (LMMN) adaptation algorithm and Symmetric Adaptive Decorrelation (SAD) structure are applied to improve the convergence rate of the adaptive filter and make a considerable improvement in the performance of the noise cancellation procedure. Also, the Voice Activity Detection (VAD) is used to reduce the computational costs and decrease the execution time. However in this study, there was an utmost attempt to consider all of the practical problems, while the minimum simplifying assumptions are made. The simulation results have proven the robustness of this algorithm compared with commonly used algorithms, in the sense of SNR and MSE improvement and speech intelligibility.