@inproceedings{81350f245a64492ab4da64f8e0b744d3,
title = "Robust Bayesian and Maximum a Posteriori Beamforming for Hearing Assistive Devices",
abstract = "Multi-microphone speech enhancement systems often apply beamforming to enhance one or multiple desired signals in a noisy environment. Common for many beamforming methods, is that they require the direction-of-arrival (DOA) of the target sound source to be known in order to achieve optimal noise reduction performance. To improve robustness against DOA uncertainty, we propose maximum a posteriori (MAP) and Bayesian beamformers that are able to take advantage of prior information on the target direction. We compare the proposed MAP and Bayesian beamformers to state-of-the-art beamforming methods for noise reduction in hearing assistive devices. We evaluate the proposed beamformers in isotropic babble noise in terms of segmental SNR (SSNR) and extended short-time objective intelligibility (ESTOI). Results show that the proposed methods outperform current state-of-the-art beamformers used for noise reduction in hearing aids in most scenarios.",
keywords = "Beamforming, Noise Reduction, Hearing Aids, Bayesian Beamforming, Maximum a posterior beamforming, Bayesian beamforming, Hearing aids, Maximum a posteriori beamforming, Noise reduction, Spatial filtering",
author = "Poul Hoang and Zheng-Hua Tan and {de Haan}, {Jan Mark} and Thomas Lunner and Jesper Jensen",
year = "2020",
month = jan,
day = "27",
doi = "10.1109/GlobalSIP45357.2019.8969234",
language = "English",
isbn = "978-1-7281-2724-8",
series = "IEEE Global Conference on Signal and Information Processing (GlobalSIP). Proceedings",
publisher = "IEEE",
booktitle = "IEEE Global Conference on Signal and Information Processing (GlobalSIP)",
address = "United States",
note = "2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), (GLobalSIP) ; Conference date: 11-11-2019 Through 14-11-2019",
}