A block least squares approach to acoustic echo cancellation

We propose an efficient block least squares (BLS) algorithm for acoustic echo cancellation. The high computation and memory requirements associated with a long room echo make the simple, gradient-based LMS filter a more acceptable commercial solution than a full-fledged LS canceler. However, the LMS echo canceler has slower convergence and worse steady-state performance than its LS counterpart. In the proposed BLS approach, the autocorrelation and cross-correlation of the source and echo, required in solving the LS normal equations, are performed once per block using FFTs. With appropriate data windowing the autocorrelation matrix is constrained to be Toeplitz, allowing the corresponding normal equations to be solved efficiently. The positive definiteness of the autocorrelation function eliminates the stability problems of other fast LS algorithms. BLS can reduce the echo residual to the level of background noise, allowing a residual power based, statistical near-end speech detector to be devised. Performance in real environments under various settings of filter length, SNR, near-end speech presence, etc., is investigated