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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020
Subtitle of host publicationSCOPES 2020
EditorsSander Stuijk
PublisherAssociation for Computing Machinery, Inc
Pages82-85
Number of pages4
ISBN (Electronic)978-1-4503-7131-5
DOIs
Publication statusPublished - 25 May 2020

Keywords

  • audio signal processing
  • Hearing impairment
  • Julia
  • openMHA
  • real time
  • digital audio signal processing
  • hearing aids

Fingerprint Dive into the research topics of 'Real-time audio processing for hearing aids using a model-based Bayesian inference framework'. Together they form a unique fingerprint.

Cite this