@article {ZHU:2024:0736-2935:8733,
title = "A low-complexity adaptive speech dereverberation approach based on multichannel linear prediction",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2024",
volume = "270",
number = "3",
publication date ="2024-10-04T00:00:00",
pages = "8733-8744",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2024/00000270/00000003/art00080",
doi = "doi:10.3397/IN_2024_4138",
author = "ZHU, Zhen and HE, Hongsen and QIU, Zhimin and YU, Yi",
abstract = "The adaptive multichannel linear prediction (AMCLP) technique is one of the most effective methods for speech dereverberation in real-time application systems. However, the computational complexity of the associated AMCLP algorithms is very high. In this paper, we propose a low-complexity
speech dereverberation approach based on AMCLP. The coefficient matrix of the multichannel dereverberation filter is decomposed into a linear combination of a set of low-dimensional sub-filter matrices and a set of low-dimensional sub-filter vectors through Kronecker product. According to
these sub-filter models, we define two sets of signal models and cost functions, from which the adaptive speech dereverberation algorithm is established. This solution not only leads to effective dereverberation performance, but also reduces the computational cost, which is very conducive
to realistic speech systems.",
}