Individual factors in speech recognition with binaural multi-microphone noise reduction: Measurement and prediction

Authors

  • Tobias Neher Medizinische Physik, Oldenburg University, Oldenburg, Germany Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Jacob Aderhold Medizinische Physik, Oldenburg University, Oldenburg, Germany Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Daniel Marquardt Signal Processing Group, Oldenburg University, Oldenburg, Germany Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Thomas Brand Medizinische Physik, Oldenburg University, Oldenburg, Germany Cluster of Excellence Hearing4all, Oldenburg, Germany

Abstract

Multi-microphone noise reduction algorithms give typically rise to large signal-to-noise ratio improvements, but they can also severely distort binaural information and thus compromise spatial hearing abilities. To address this problem Klasen et al. (2007) proposed an extension of the binaural multi-channel Wiener filter (MWF), which suppresses only part of the noise and, in this way, preserves some binaural information (MWF-N). The current study had three aims: (1) to assess aided speech recognition with MWF(-N) for a group of elderly hearing-impaired listeners, (2) to explore the impact of individual factors on their performance, and (3) to test if outcome can be predicted using a binaural speech intelligibility model. Sixteen hearing aid users took part in the study. Speech recognition was assessed using headphone simulations of a spatially complex speech-in-noise scenario. Individual factors were assessed using audiometric, psychoacoustic (binaural), and cognitive measures. Analyses showed clear benefits from both MWF and MWF-N and also suggested sensory and binaural influences on speech recognition. Model predictions were reasonably accurate for MWF but not MWF-N, suggesting a need for some model refinement concerning supra-threshold processing.

References

ANSI (1997). “Methods for calculation of the speech intelligibility index (S3.5-1997),” New York, NY: American National Standards Institute.

Beutelmann, R., Brand, T., and Kollmeier, B. (2010). “Revision, extension, and evaluation of a binaural speech intelligibility model,” J. Acoust. Soc. Am., 127, 2479-2497.

Bronkhorst, A.W. (2015). “The cocktail-party problem revisited: Early processing and selection of multi-talker speech,” Atten. Percept. Psycho., 77, 1465-1487.

Byrne, D., Parkinson, A., and Newall, P. (1991). “Modified hearing aid selection procedures for severe/profound hearing losses,” in The Vanderbilt Hearing Aid Report II. Eds. G.A. Studebaker, F.H. Bess, and L.B. Beck (York Press, Parkton, NC), pp. 295-300.

Carroll, R., Meis, M., Schulte, M., Vormann M, Kießling J, and Meister H (2015). “Development of a German reading span test with dual task design for application in cognitive hearing research,” Int. J. Audiol., 54, 136-141.

Dietz, M., Ewert, S.D., and Hohmann, V. (2011). “Auditory model based direction estimation of concurrent speakers from binaural signals,” Speech Comm., 53, 592-605.

Doclo, S., Klasen, T.J., Wouters, J., Haykin, S., and Moonen, M. (2006). “Theoretical analysis of binaural cue preservation using multi-channel Wiener filtering and interaural transfer functions,” Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), Paris, France, Sept. 12-14.

Doclo, S., Gannot, S., Moonen, M., and Spriet, A. (2010). “Acoustic beamforming for hearing aid applications,” in Handbook on Array Processing and Sensor Networks. Eds. S. Haykin and K.J.R. Liu (Wiley-IEEE Press, Hoboken, NJ), pp. 269-302.

Durlach, N.I. (1963). “Equalization and cancellation theory of binaural masking-level differences,” J. Acoust. Soc. Am., 35, 1206-1218.

Kayser, H., Ewert, S.D., Anemüller, J., Rohdenburg, T., Hohmann, V., and Kollmeier, B. (2009). “Database of multichannel in-ear and behind-the-ear head-related and binaural room impulse responses,” EURASIP J. Adv. Signal Process., 298605, DOI: 10.1155/2009/298605.

Klasen, T.J., van den Bogaert, T., Moonen, M., and Wouters, J. (2007). “Binaural noise reduction algorithms for hearing aids that preserve interaural time delay cues,” IEEE Trans. Signal Process., 55, 1579-1585.

Neher, T., Laugesen, S., Jensen, N.S., and Kragelund, L. (2011). “Can basic auditory and cognitive measures predict hearing-impaired listeners’ localization and spatial speech recognition abilities?” J. Acoust. Soc. Am., 130, 1542-1558.

Neher, T., Lunner, T., Hopkins, K., and Moore, B.C.J. (2012). “Binaural temporal fine structure sensitivity, cognitive function, and spatial speech recognition of hearing-impaired listeners,” J. Acoust. Soc. Am., 131, 2561-2564.

Santurette, S. and Dau, T. (2012). “Relating binaural pitch perception to the individual listener’s auditory profile,” J. Acoust. Soc. Am., 131, 2968-2986.

van den Bogaert, T., Doclo, S., Wouters, J., and Moonen, M. (2008). “The effect of multimicrophone noise reduction systems on sound source localization by users of binaural hearing aids,” J. Acoust. Soc. Am., 124, 484-497.

Wagener, K., Brand, T., and Kollmeier, B. (1999). “Entwicklung und Evaluation eines Satztests für die deutsche Sprache. I-III: Design, Optimierung und Evaluation des Oldenburger Satztests”, Zeitschrift für Audiologie, 38, 4-15, 44-56, 86-95.

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Published

2015-12-15

How to Cite

Neher, T., Aderhold, J., Marquardt, D., & Brand, T. (2015). Individual factors in speech recognition with binaural multi-microphone noise reduction: Measurement and prediction. Proceedings of the International Symposium on Auditory and Audiological Research, 5, 237–244. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2015-27

Issue

Section

2015/4. Compensation strategies for hearing rehabilitation with hearing aids