Acoustic echo cancellation using a computationally efficient transform domain LMS adaptive filter

Abstract

Applications such as hands-free telephony, tele-classing and video-conferencing require the use of an acoustic echo canceller (AEC) to eliminate acoustic feedback from the loudspeaker to the microphone. Room acoustic echo cancellation typically requires adaptive filters with thousands of coefficients. Transform domain adaptive filter finds best solution for echo cancellation as it results in a significant reduction in the computational burden. Literature finds different orthogonal transform based adaptive filters for echo cancellation. In this paper, we present Hirschman Optimal Transform (HOT) based adaptive filter for elimination of echo from audio signals. Simulations and analysis show that HOT based LMS adaptive filter is computationally efficient and has fast convergence compared to LMS, NLMS and DFT-LMS. The computed Echo Return Loss Enhancement (ERLE), the general evaluation measure of echo cancellation, established the efficacy of proposed HOT based adaptive algorithm. In addition, the spectral flatness measure showed a significant improvement in cancelling the acoustic echo.