kjsanchez / openmha

The open Master Hearing Aid (openMHA)

openMHA

Open Master Hearing Aid (openMHA)

Current release: 4.17.0 (2022-02-24)

Content of the openMHA

The software contains the source code of the openMHA Toolbox library, of the openMHA framework and command line application, several tools to operate openMHA and of a selection of algorithm plugins forming a basic hearing aid processing chain featuring

• calibration
• bilateral adaptive differential microphones for noise suppression [1]
• binaural coherence filter for feedback reduction and dereverberation [2]
• multi-band dynamic range compressor for hearing loss compensation [3]
• spatial filtering algorithms:
  • a delay-and-sum beamformer
  • a MVDR beamformer [4]
• single-channel noise reduction [5]
• resampling and filter plugins
• STFT cyclic aliasing prevention
• adaptive feedback cancellation [6]
• probabilistic sound source localization [7]

See below for a list of available reference implementations.

Citation in publications

In publications using openMHA please use the DOI assigned to this Github repository and cite the following open-access publication:

Hendrik Kayser, Tobias Herzke, Paul Maanen, Max Zimmermann, Giso Grimm, and Volker Hohmann, Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA), SoftwareX, Volume 17, 2022, 100953, ISSN 2352-7110, DOI: 10.1016/j.softx.2021.100953.

For individual algorithms, please also refer to the plugin documentation and the list of publications at the end of this README.

Installation

For installation instructions for Linux, Windows and macOS, please refer to the instructions in file INSTALLATION.md.

We also provide SD card images for Beaglebone Black with the Cape4all sound card here: http://mahalia.openmha.org/.

Usage instructions:

Please follow our getting-started guide: http://www.openmha.org/docs/openMHA_starting_guide.pdf

Our user forum is found here: https://forum.openmha.org/

Known issues

macOS

• There are some known issues with Octave under macOS. The openMHA GUI may not work correctly with octave. As an alternative Matlab can be used.

Proprietary fitting rules

It is possible to fit a dynamic compressor in openMHA with the commercial hearing aid prescription rules DSLmio 5 and NAL NL2.

The software libraries implementing these rules must be obtained from their respective authors. The openMHA team provides wrappers around these libraries which for legal reasons are not distributed as part of openMHA but as optional extras.

Please refer to files README_NALNL2.md and README_DSLmio5.md for more information.

Reference algorithms

A collection of openMHA configuration files that implement signal processing algorithms for hearing aids as they were used in the following publications are available in the reference_algorithms directory:

Baumgärtel, R. M., Krawczyk-Becker, M., Marquardt, D., Völker, C., Hu, H., Herzke, T., Coleman, G., Adiloğlu, K., Ernst, S. M., Gerkmann, T., Doclo, S., Kollmeier, B., Hohmann, V., & Dietz, M. (2015). Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation. Trends in hearing, 19. https://doi.org/10.1177/2331216515617916

and

Hendrikse, M. M. E., Grimm, G., & Hohmann, V. (2020). Evaluation of the Influence of Head Movement on Hearing Aid Algorithm Performance Using Acoustic Simulations. Trends in Hearing, 24, 1–20. https://doi.org/10.1177/2331216520916682

A database that can be utilized to reproduce the signals used in the latter study is available under: https://doi.org/10.5281/zenodo.3621282.

Available methods:

• Single-channel noise reduction
• Binaural coherence filter
• Adaptive MVDR beamformer
• Binaural beamformer
• Bilateral adaptive differential microphones
• Delay-and-subtract beamformer

For references and more information see README.txt in the reference_algorithms directory.

References for individual algorithms

[1] Elko GW, Pong ATN. A Simple Adaptive First-order Differential Microphone. In: Proceedings of 1995 Workshop on Applications of Signal Processing to Audio and Accoustics; 1995. p. 169–172.

[2] Grimm G, Hohmann V, Kollmeier B. Increase and Subjective Evaluation of Feedback Stability in Hearing Aids by a Binaural Coherence-based Noise Reduction Scheme. IEEE Transactions on Audio, Speech, and Language Processing. 2009;17(7):1408–1419.

[3] Grimm G, Herzke T, Ewert S, Hohmann V. Implementation and Evaluation of an Experimental Hearing Aid Dynamic Range Compressor Gain Prescription. In: DAGA 2015; 2015. p. 996–999.

[4] Adiloğlu K, Kayser H, Baumgärtel RM, Rennebeck S, Dietz M, Hohmann V. A Binaural Steering Beamformer System for Enhancing a Moving Speech Source. Trends in Hearing. 2015;19:2331216515618903

[5] Gerkmann T, Hendriks RC. Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay. IEEE Transactions on Audio, Speech, and Language Processing. 2012;20(4):1383–1393.

[6] Schepker H, Doclo S, A semidefinite programming approach to min-max estimation of the common part of acoustic feedback paths in hearing aids. IEEE Transactions on Audio, Speech, and Language Processing. 2016;24(2):366-377.

[7] Kayser H, Anemüller J, A discriminative learning approach to probabilistic acoustic source localization. In: International Workshop on Acoustic Echo and Noise Control (IWAENC 2014); 2014. p. 100–104.