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Starting from the characteristic of voice signal itself, a new variable step-size LMS algorithm based on DCT is proposed in order to improve the comprehensive performance in adaptive noise cancellation, which combines the merits of normalized DCT-LMS algorithm and variable step-size LMS algorithm, and give full play to the decorrelation capability of DCT and rapid convergence effect of variable step-size algorithm. The c simulation results show that the new algorithm has faster convergence rate and smaller steady state error compared with traditional LMS and NLMS algorithm, however, the computational complexity is comparable to NLMS algorithm at the same time. Introduction With the rapid development of communication technology, people's demand for quality of voice signal is growing daily, As a specific branch of adaptive signal processing, Adaptive noise cancellation technology(ANC) [1,2]can extract required signal from the loud background noise. The theoretical framework of ANC was first presented by Widrow and Hoff in 1967, now it has been applied in a great many areas such as voice signal denoising, interference cancellation in biomedical detection and echo cancellation in communication lines. Theory of Adaptive Noise Cancellation The original signal port receives the signal ( ) s n from signal source which is corrupted by the noise ( ) g n from noise source at the same time, the reference input port receives the noise ( ) X n which is correlated with the noise ( ) g n in some way, but uncorrelated with the signal ( ) s n . According to correlation of noise itself and independence between signals, if subtracting the filter output signal ( ) y n from the noise ( ) g n and making them approaching gradually, in ideal case, the error signal ( ) e n can be regarded as the optimum estimation of the useful signal ( ) s n . As the core of the system, the adaptive filter can achieve an optimum filter effect by using of algorithm to update the filter coefficients in realtime. Now there are many algorithms improved on the basis of traditional LMS algorithm, these algorithms fall into 2 categories, one is variable step-size LMS algorithm[3] which builds functional relationships between step-size factor and the error signal ( ) e n to solve the immanent contradictions between convergence rate and steady state error. The other is transform domain LMS algorithm[4] which can reduce the eigenvalue distribution of the autocorrelation matrix to optimize the convergence rate and robustness by processing the input signal with some orthogonal transform. The performance indexes of several adaptive filtering algorithms are compared with each other in this paper, then a new variable step-size LMS algorithm based on Discrete Cosine Transform(DCT) is proposed which combines the merits of each one. Add real voice signal into adaptive noise cancellation system to do the experiments. The results show that the new algorithm has the best performance to achieve the purpose of denoising.

Variable Step-size LMS Algorithm Based on Discrete Cosine Transform for Voice Signal Denoising