Interference control in speech using efficient subband LMS filtering

In this paper, an efficient interference canceller is developed using subband adaptive filtering. The aim is to reduce problems incorporated with the use of the conventional least mean square LMS adaptive algorithm in noise cancellation setup. The subband model is obtained by inserting a computationally efficient two fold oversampled filter banks in the conventional fullband model. The prototype filter of the filter bank is optimized for minimum amplitude distortion. Variable step-size LMS adaptive filters are used in subbands. Mean square error MSE convergence is used as a measure of performance under white and environmental noise. Compared to equivalent fullband and critically sampled systems, fast initial convergence is obtained. In addition to that, the amount of noise cancellation is improved by 10-15 dB on steady state.