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.
|Titel||Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020|
|Uitgeverij||Association for Computing Machinery, Inc|
|ISBN van elektronische versie||978-1-4503-7131-5|
|Status||Gepubliceerd - 25 mei 2020|