In this paper, an improved method of reducing ambient noise in speech signals is introduced. The proposed noise canceller was developed using a computationally efficient (DFT) filter bank to decompose input signals into sub-bands. The filter bank was based on a prototype filter optimized for minimum output distortion. A variable step-size version of the (LMS) filter was used to reduce the noise in individual branches. The subband noise canceller was aimed to overcome problems associated with the use of the conventional least mean square (LMS) adaptive algorithm in noise cancellation setups. Mean square error convergence was used as a measure of performance under white and ambient interferences. Compared to conventional as well as recently developed techniques, fast initial convergence and better noise cancellation performances were obtained under actual speech and ambient noise.