Convergence improvement for oversampled subband adaptive noise and echo cancellation

The convergence rate of the Least Mean Square (LMS) algorithm is dependent on the eigenvalue distribution of the reference input correlation matrix. When adaptive filters are employed in low-delay over-sampled subband structures, colored subband signals considerably decelerate the convergence speed. Here, we propose and implement two promising techniques for improving the convergence rate based on: 1) Spectral emphasis and 2) Decimation of the subband signals. We analyze the effects of the proposed methods based on theoretical relationships between eigenvalue distribution and convergence characteristics. We also propose a combined decimation and spectral emphasis whitening technique that exploits the advantages of both methods to dramatically improve the convergence rate. Moreover, through decimation the combined whitening approach reduces the overall computation cost compared to subband LMS with no preprocessing. Presented theoretical and simulation results confirm the effectiveness of the proposed convergence improvement methods.