short-paper

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

Authors:

Martin Roa Villescas,

Henk CorporaalAuthors Info & Claims

SCOPES '20: Proceedings of the 23th International Workshop on Software and Compilers for Embedded Systems

Pages 82 - 85

https://doi.org/10.1145/3378678.3397528

Published: 25 May 2020 Publication History

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.

References

[1]

J Agnew and J Thornton. 2000. Just noticeable and objectionable group delays in digital hearing aids. Journal of the American Academy of Audiology 11 (07 2000), 330--336.

[2]

William Audette, Odile Clavier, Daniel Rasetshwane, Stephen T. Neely, and Joel Murphy. 2017. Development of an open source audio processing platform. The Journal of the Acoustical Society of America 141, 5 (2017), 3895--3896. arXiv:https://doi.org/10.1121/1.4988748

[3]

Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, and Noah D. Goodman. 2019. Pyro: Deep Universal Probabilistic Programming. J. Mach. Learn. Res. 20 (2019), 28:1--28:6. http://jmlr.org/papers/v20/18-403.html

[4]

Marco Cox, Thijs van de Laar, and Bert de Vries. 2019. A factor graph approach to automated design of Bayesian signal processing algorithms. International Journal of Approximate Reasoning 104 (Jan. 2019), 185--204.

[5]

Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, and Vikash K. Mansinghka. 2019. Gen: A General-Purpose Probabilistic Programming System with Programmable Inference. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (Phoenix, AZ, USA) (PLDI 2019). Association for Computing Machinery, New York, NY, USA, 221--236.

Digital Library

[6]

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, and Rif A. Saurous. 2017. TensorFlow Distributions. ArXiv abs/1711.10604 (2017).

[7]

Hong Ge, Kai Xu, and Zoubin Ghahramani. 2018. Turing: A Language for Flexible Probabilistic Inference. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), Amos Storkey and Fernando Perez-Cruz (Eds.), Vol. 84. PMLR, Playa Blanca, Lanzarote, Canary Islands, 1682--1690. http://proceedings.mlr.press/v84/ge18b.html

[8]

Tobias Herzke, Hendrik Kayser, Frasher Loshaj, Giso Grimm, and Volker Hohmann. 2017. Open signal processing software platform for hearing aid research (openMHA). In Proceedings on the Linux Audio Conference. Université Jean Monnet, Saint-Étienne, France, 35--42. http://www.openmha.org/

[9]

Tobias Herzke, Hendrik Kayser, Christopher Seifert, Paul Maanen, Christopher Obbard, Guillermo Payá-Vayá, Holger Blume, and Volker Hohmann. 2018. Open Hardware Multichannel Sound Interface for Hearing Aid Research on BeagleBone Black with openMHA: Cape4all. In Proceedings of the Linux Audio Conference 2018. c-base, in partnership with the Electronic Studio at TU Berlin, Berlin, Germany.

[10]

David Moffat, David Ronan, and Joshua Reiss. 2015. An Evaluation of Audio Feature Extraction Toolboxes. In Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15). NTNU: Norwegian University of Science and Technology, Trondheim, Norway.

[11]

Judea Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

[12]

L. Pisha, J. Warchall, T. Zubatiy, S. Hamilton, C. Lee, G. Chockalingam, P. P. Mercier, R. Gupta, B. D. Rao, and H. Garudadri. 2019. A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research. IEEE Access 7 (2019), 162083--162101.

[13]

Simon Danisch. 2020. PackageCompiler. Osnabruck University, Berlin, Germany. https://github.com/JuliaLang/PackageCompiler.jl

[14]

Michael A. Stone and Bcj Moore. 1999. Tolerable hearing aid delays. I. Estimation of limits imposed by the auditory path alone using simulated hearing losses. Ear and hearing 20 3 (1999), 182--92.

[15]

Timothy E. Holy. 2020. SnoopCompile. Version 1.2.3. Washington University, St. Louis, Missouri, USA. https://github.com/timholy/SnoopCompile.jl

[16]

T. Zhang, F. Mustiere, and C. Micheyl. 2016. Intelligent hearing aids: The next revolution. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Orlando, FL, 72--76.

Cited By

Pal SBhattacharya MDash SLee SChakraborty C(2024)A next-generation dynamic programming language Julia: Its features and applications in biological scienceJournal of Advanced Research10.1016/j.jare.2023.11.01564(143-154)Online publication date: Oct-2024
https://doi.org/10.1016/j.jare.2023.11.015

Index Terms

Real-time audio processing for hearing aids using a model-based bayesian inference framework
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
2. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
  2. Real-time systems

Recommendations

Low-Delay Signal Processing for Digital Hearing Aids

Digital signal processing in modern hearing aids is typically performed in a subband or transform domain that introduces analysis-synthesis delays in the forward path. Long forward-path delays are not desirable because the processed sound combines with ...
Theoretical analysis of linearly constrained multi-channel wiener filtering algorithms for combined noise reduction and binaural cue preservation in binaural hearing aids

Besides noise reduction, an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. For the desired speech source and the interfering sources, e.g., competing speakers, this can be ...
Interaural coherence preservation in multi-channel wiener filtering-based noise reduction for binaural hearing aids

Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. To this end, an extension of the binaural multi-channel Wiener filter (MWF), namely the MWF-ITF, has ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SCOPES '20: Proceedings of the 23th International Workshop on Software and Compilers for Embedded Systems

May 2020

96 pages

ISBN:9781450371315

DOI:10.1145/3378678

Editor:
Sander Stuijk
Eindhoven University of Technology, The Netherlands
,
General Chair:
Henk Corporaal
Eindhoven University of Technology, NL

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGBED: ACM Special Interest Group on Embedded Systems

In-Cooperation

EDAA: European Design Automation Association

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Conference

SCOPES '20

Sponsor:

SIGBED

SCOPES '20: 23rd International Workshop on Software and Compilers for Embedded Systems

May 25 - 26, 2020

St. Goar, Germany

Acceptance Rates

SCOPES '20 Paper Acceptance Rate 8 of 13 submissions, 62%;

Overall Acceptance Rate 38 of 79 submissions, 48%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
191
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pal SBhattacharya MDash SLee SChakraborty C(2024)A next-generation dynamic programming language Julia: Its features and applications in biological scienceJournal of Advanced Research10.1016/j.jare.2023.11.01564(143-154)Online publication date: Oct-2024
https://doi.org/10.1016/j.jare.2023.11.015

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten