GPU Implementation of multichannel adaptive algorithms for local active noise control

Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processi...

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Bibliographic Details
Authors: Lorente Giner, Jorge; Ferrer Contreras, Miguel|||0000-0002-8743-1887; de Diego Antón, María|||0000-0001-9948-3396; Gonzalez, Alberto|||0000-0002-6984-3212
Format: Article
Publication date:2014
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/51611
Online Access:https://riunet.upv.es/handle/10251/51611
Access Level:Open access
Keyword:Active noise control
Filtered-x least mean square
Graphics processing unit
TEORIA DE LA SEÑAL Y COMUNICACIONES
Description
Summary:Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.