Kenig, OriTodros, KobyAdali, Tulay2023-07-072023-07-072023-05-05O. Kenig, K. Todros and T. Adali, "Robust GMM Parameter Estimation via the K-BM Algorithm," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094602.https://doi.org/10.1109/ICASSP49357.2023.10094602http://hdl.handle.net/11603/285102023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 04-10 June 2023In this paper, we develop an expectation-maximization (EM)-like scheme, called K-BM, for iterative numerical computation of the minimum K-divergence estimator (MKDE). This estimator utilizes Parzen’s non-parameteric Kernel density estimate to down weight low density areas attributed to outliers. Similarly to the standard EM algorithm, the K-BM involves successive Maximizations of lower Bounds on the objective function of the MKDE. Differently from EM, these bounds do not rely on conditional expectations only. The proposed K-BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). Simulation studies illustrate the performance advantage of the K-BM as compared to other state-of-the-art robust GMM estimators.5 pagesen-US© 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Text