Real-time audio processing for hearing aids using a model-based Bayesian inference framework

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Development of hearing aid (HA) signal processing algorithms entails an iterative process between two design steps, namely algorithm development and the embedded implementation. Algorithm designers favor high-level programming languages for several reasons including higher productivity, code readability and, perhaps most importantly, availability of state-of-the-art signal processing frameworks that open new research directions. Embedded software, on the other hand, is preferably implemented using a low-level programming language to allow finer control of the hardware, an essential trait in real-time processing applications. In this paper we present a technique that allows deploying DSP algorithms written in Julia, a modern high-level programming language, on a real-time HA processing platform known as openMHA. We demonstrate this technique by using a model-based Bayesian inference framework to perform real-time audio processing.
Originele taal-2Engels
TitelProceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020
SubtitelSCOPES 2020
RedacteurenSander Stuijk
UitgeverijAssociation for Computing Machinery, Inc
Pagina's82-85
Aantal pagina's4
ISBN van elektronische versie978-1-4503-7131-5
DOI's
StatusGepubliceerd - 25 mei 2020

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