Nonlinear acoustic echo cancellation with kernelized adaptive filters. (September 2020)

• Record Type: Journal Article
• Authors: Sankar, Sanjana; Kar, Asutosh; Burra, Srikanth; Swamy, M.N.S.; Mladenovic, Vladimir
• Is Part Of: Applied acoustics. Volume 166(2020)
• Publication Date: 2020-09
• Subjects: Kernel adaptive filters -- Nonlinear acoustic echo cancellation -- Normalized least mean square -- Eho return loss enhancement; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2020.107329
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: A well-known problem in speech communication is the occurrence of acoustic echo in hands-free telephony. There are several classical adaptive filters that have been used as a stand alone approach for linear acoustic echo cancellation(AEC). A common assumption in most AEC techniques is that the echo path is linear. However, in real scenarios, owing to distortions introduced by other acoustic artefacts in handheld devices, the echo path is nonlinear. Therefore, there is a need to employ a nonlinear echo canceller. In this paper, a kernel expansion on a family of adaptive filtering algorithms based on least mean square filter is proposed. Kernel methods help model the nonlinear echo path and attain global minimum easily by finding the optimal set of filter weights. The simulations are carried out for speech signal with and without noise under different signal-to-noise ratio values. The results show that the proposed method achieves a significant improvement in a nonlinear acoustic echo cancellation environment in terms of echo return loss enhancement.