Improved, Low Complexity Noise Cancellation Technique for Speech Signals

A noise cancellation system with an improved performance and low computational costs is presented in this paper. In speech applications, slow convergence and high computational burden are the main problems incorporating with conventional noise cancellation method. The proposed noise canceller is based on using multirate filter bank to split the spectrum of the input signals and uses the least mean square (LMS) algorithm in branches to control a finite impulse response (FIR) filter to reduce the noise in the input noisy speech. The computational power is greatly reduced by polyphase implementation and the noble identities. Direct and polyphase models were developed, tested and compared to the equivalent full band model. The proposed scheme shows better convergence behavior compared to classical approach with 50% reduction in computational complexity.