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HörTech Open Master Hearing Aid (openMHA)

Content of the openMHA release 4.14.0 (2021-02-15)

The software contains the source code of the openMHA Toolbox library, of the openMHA framework and command line application, 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 cite

Herzke, T., Kayser, H., Loshaj, F., Grimm, G., Hohmann, V., Open signal processing software platform for hearing aid research (openMHA). Proceedings of the Linux Audio Conference. Université Jean Monnet, Saint-Étienne, pp. 35-42, 2017.

As we are working on an updated paper, please check back this section of the README for updates.

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


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

We also provide SD card images for Beaglebone Black with the Cape4all sound card here:

Usage instructions:

Please follow our getting-started guide:

Known issues


  • 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.

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.


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.

A database that can be utilized to reproduce the signals used in the latter study is available under:

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.