Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids

Abstract

Objective: Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Design: Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. Study sample: The perceptual investigation was performed with fourteen hearing-impaired subjects. Results: The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Conclusion: Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Key Words::

• Noise reduction
• hearing aids
• non-stationary noise
• hearing impairment
• model-based

Acknowledgements

This work was supported by the German Federal Ministry of Education and Research under project number 01EZ0741 “Model-based hearing systems”. Many thanks to Birger Kollmeier for continuous support and discussions. The authors would also like to thank Matthias Vormann for performing parts of the measurements and Rainer Huber, Tobias Herzke, Giso Grimm, Dirk Mauler, Tobias May and Darrin Reed for helpful comments on earlier versions of the manuscript.

Notes

1. PEMO simulates the detection characteristics of the human auditory system. It comprises several partly nonlinear processing stages and transforms the input signal into an ܁internal܀ representation that describes the signal-related information that is available to the brain.

2. Sigma-pi cells are second order elements from neural networks which multiply weighted outputs from several units before summation over all input values. Applied to spectro-temporal patterns, they detect and exploit signal-induced correlation across time and frequency.

3. The source of this babble is 100 people speaking in a cafeteria

4. http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html

5. International Collegium of Rehabilitative Audiology: male-spectrum-shaped wide-band noise that contains envelope modulations and maximum silent gap length of 625 ms.

6. Recorded in a canteen by Siemens Audiologische Technik GmbH, Erlangen.

7. Recorded close to a street by Siemens Audiologische Technik GmbH, Erlangen.

8. OlSa noise is a speech shaped noise, which corresponds to the long term spectrum of the OlSa speech material.

9. http://www.ubuntu.com/desktop/get-ubuntu/download

10. www.hoertech.de

11. Explained in Fredelake & Holube (2010).

Biographical notes

Niklas Harlander received the Dipl.-Ing. (FH) degree in hearing technology and audiology from Oldenburg University of Applied Science, Oldenburg, Germany, in 2005 and the M.Sc. degree in hearing technology and audiology from the University of Oldenburg, Oldenburg, Germany, in 2007. He is currently working as a research associate at the Medical Physics department of the University of Oldenburg. His research interests include classification, noise reduction and perceptual quality assessment of audio signals.

Tobias Rosenkranz received his Dipl.-Ing. degree in electrical engineering, electronics, and information technology from the University of Erlangen-Nuremberg, Germany in 2008. Since 2008 he is with Siemens Audiologische Technik, Erlangen, Germany, where he pursues his Dr.-Ing. degree. His main research interests are speech enhancement, noise reduction, and source localization.

Volker Hohmann received the Physics degree (Dipl.-Phys.) and the doctorat degree in physics (Dr. rer. nat.) from the University of Göttingen, Göttingen, Germany, in 1989 and 1993, respectively. Since 1993, he has been a Faculty Member of the Physics Institute, University of Oldenburg, Oldenburg, Germany, and member of the Medical Physics Group (Prof. B. Kollmeier). He is a consultant with the Hörzentrum Oldenburg GmbH. He was a Guest Researcher at Boston University, Boston, MA, (Prof. Dr. Colburn) in 2000 and at the Technical University of Catalonia, Barcelona, Spain in 2008. Dr. Hohmann received the Lothar-Cremer price of the German acoustical society (DEGA) in 2008.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.