Hardware / Software Architecture for Services in the Hearing Aid Industry

With the growing number of people affected by hearing loss, large research and development efforts have been carried to improve the performance of hearing aids. Although these efforts have proven fruitful, e.g., by improving the speech enhancement algorithms, further work is needed to improve the automatic and individualized selection of such algorithms, enhance services provided by the hearing aid acousticians to hearing aid users, and support their work by increasing the connectivity of hearing aids. This paper provides a brief overview of the connectivity features available in modern hearing aids as well as the recent developments in acoustic scenes classifiers before describing an hardware / software architecture designed to exploit advances made in both those fields. This proposed architecture offers support to both hearing aid users and professionals of the hearing aid industry.

[1]  Bhiksha Raj,et al.  Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording , 2016, DCASE.

[2]  Monica Anderson and Andrew Perrin.  Tech Adoption Climbs Among Older Adults , 2017 .

[3]  Birger Kollmeier,et al.  An Auditory Inspired Amplitude Modulation Filter Bank for Robust Feature Extraction in Automatic Speech Recognition , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[4]  Gerhard Widmer,et al.  CP-JKU SUBMISSIONS FOR DCASE-2016 : A HYBRID APPROACH USING BINAURAL I-VECTORS AND DEEP CONVOLUTIONAL NEURAL NETWORKS , 2016 .

[5]  Brent Edwards,et al.  The Future of Hearing Aid Technology , 2007, Trends in amplification.

[6]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[7]  P. von Gablenz,et al.  Prävalenz von Schwerhörigkeit im Nordwesten Deutschlands , 2015, HNO.

[8]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[9]  Marc Moonen,et al.  Binaural Integrated Active Noise Control and Noise Reduction in Hearing Aids , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Marc Moonen,et al.  Binaural Noise Reduction Algorithms for Hearing Aids That Preserve Interaural Time Delay Cues , 2007, IEEE Transactions on Signal Processing.

[11]  Jason Galster,et al.  A Qualitative Case Study of Smartphone‐Connected Hearing Aids: Influences on Patients, Clinicians, and Patient‐Clinician Interactions , 2017, Journal of the American Academy of Audiology.

[12]  Jörn Anemüller,et al.  Automatic acoustic siren detection in traffic noise by part-based models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  R. Beutelmann,et al.  Prediction of speech intelligibility in spatial noise and reverberation for normal-hearing and hearing-impaired listeners. , 2006, The Journal of the Acoustical Society of America.

[14]  Dan Stowell,et al.  A database and challenge for acoustic scene classification and event detection , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[15]  Timothy H. Riehle,et al.  Tri-axial telecoil hearing aid for improved connection to public induction loops , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Alain Rakotomamonjy,et al.  Histogram of gradients of Time-Frequency Representations for Audio scene detection , 2015 .

[17]  Jörn Anemüller,et al.  Spectro-Temporal Gabor Filterbank Features for Acoustic Event Detection , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[18]  Alain Rakotomamonjy,et al.  Histogram of gradients of Time-Frequency Representations for Audio scene detection , 2015, ArXiv.

[19]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[20]  Guy J. Brown,et al.  Computational auditory scene analysis , 1994, Comput. Speech Lang..

[21]  S. Essid,et al.  SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION , 2016 .

[22]  Henning Puder,et al.  Binaural Signal Processing in Hearing Aids: Technologies and Algorithms , 2008 .

[23]  Tuomas Virtanen,et al.  TUT Acoustic scenes 2016, Development dataset , 2016 .

[24]  Mathieu Lagrange,et al.  Saliency-based modeling of acoustic scenes using sparse non-negative matrix factorization , 2013, 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS).

[25]  R K Surr,et al.  Comparison of benefits provided by different hearing aid technologies. , 2000, Journal of the American Academy of Audiology.

[26]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.