Analysis of compressive properties of the BioAid hearing aid algorithm

Abstract Objective: This technical paper describes a biologically inspired hearing aid algorithm based on a computer model of the peripheral auditory system simulating basilar membrane compression, reflexive efferent feedback and its resulting properties. Design: Two evaluations were conducted on the core part of the algorithm, which is an instantaneous compression sandwiched between the attenuation and envelope extraction processes of a relatively slow feedback compressor. Study sample: The algorithm’s input/output (I/O) function was analysed for different stationary (ambient) sound levels, and the algorithm’s response to transient sinusoidal tone complexes was analysed and contrasted to that of a reference dynamic compressor. Results: The algorithm’s emergent properties are: (1) the I/O function adapts to the average sound level such that processing is linear for levels close to the ambient sound level and (2) onsets of transient signals are marked across time and frequency. Conclusion: Adaptive linearisation and onset marking, as inherent compressive features of the algorithm, provide potentially beneficial features to hearing-impaired listeners with a relatively simple circuit. The algorithm offers a new, biological perspective on hearing aid amplification.

[1]  Florian Langner,et al.  Forward-Masked Frequency Selectivity Improvements in Simulated and Actual Cochlear Implant Users Using a Preprocessing Algorithm , 2016, Trends in hearing.

[2]  E. Lopez-Poveda,et al.  A human nonlinear cochlear filterbank. , 2001, The Journal of the Acoustical Society of America.

[3]  Watjana Lilaonitkul,et al.  Reflex control of the human inner ear: a half-octave offset in medial efferent feedback that is consistent with an efferent role in the control of masking. , 2009, Journal of neurophysiology.

[4]  Ray Meddis,et al.  A computer model of medial efferent suppression in the mammalian auditory system. , 2007, The Journal of the Acoustical Society of America.

[5]  M. Liberman,et al.  Response properties of cochlear efferent neurons: monaural vs. binaural stimulation and the effects of noise. , 1988, Journal of neurophysiology.

[6]  M. Davies,et al.  Endovascular treatment of tracheoinnominate artery fistula: a case report. , 2006, Vascular and endovascular surgery.

[7]  J. Guinan,et al.  Time-course of the human medial olivocochlear reflex. , 2006, The Journal of the Acoustical Society of America.

[8]  Giso Grimm,et al.  Implementation and evaluation of an experimental hearing aid dynamic range compressor , 2015 .

[9]  Jan Wouters,et al.  The potential of onset enhancement for increased speech intelligibility in auditory prostheses. , 2012, The Journal of the Acoustical Society of America.

[10]  Giso Grimm,et al.  Auditory Model-Based Dynamic Compression Controlled by Subband Instantaneous Frequency and Speech Presence Probability Estimates , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[11]  P E Souza,et al.  Improving audibility with nonlinear amplification for listeners with high-frequency loss. , 2000, Journal of the American Academy of Audiology.

[12]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[13]  Enrique A. Lopez-Poveda,et al.  A Binaural Cochlear Implant Sound Coding Strategy Inspired by the Contralateral Medial Olivocochlear Reflex , 2015, Ear and hearing.

[14]  J. Guinan,et al.  Separate mechanical processes underlie fast and slow effects of medial olivocochlear efferent activity , 2003, The Journal of physiology.

[15]  Stephan M. A. Ernst,et al.  Binaural model-based dynamic-range compression , 2018, International journal of audiology.

[16]  M. C. Brown.  Morphology and response properties of single olivocochlear fibers in the guinea pig , 1989, Hearing Research.

[17]  I. Whitfield.  Discharge Patterns of Single Fibers in the Cat's Auditory Nerve , 1966 .

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

[19]  B Kollmeier,et al.  The effect of multichannel dynamic compression on speech intelligibility. , 1995, The Journal of the Acoustical Society of America.

[20]  I. Russell,et al.  Medial efferent inhibition suppresses basilar membrane responses to near characteristic frequency tones of moderate to high intensities. , 1997, The Journal of the Acoustical Society of America.

[21]  D. Markle,et al.  Hearing Aids , 1936, The Journal of Laryngology & Otology.

[22]  Marc A Brennan,et al.  The influence of hearing-aid compression on forward-masked thresholds for adults with hearing loss. , 2015, The Journal of the Acoustical Society of America.

[23]  Tim Jürgens,et al.  A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise. , 2012, The Journal of the Acoustical Society of America.

[24]  Enrique A Lopez-Poveda,et al.  Across-frequency behavioral estimates of the contribution of inner and outer hair cell dysfunction to individualized audiometric loss , 2014, Frontiers in neuroscience.

[25]  Mario A. Ruggero,et al.  Two-tone distortion products in the basilar membrane of the chinchilla cochlea , 1990 .

[26]  Giso Grimm,et al.  The master hearing Aid : A PC-based platform for algorithm development and evaluation , 2006 .

[27]  P. Blamey.  Adaptive Dynamic Range Optimization (ADRO): A Digital Amplification Strategy for Hearing Aids and Cochlear Implants , 2005, Trends in amplification.

[28]  W A Dreschler,et al.  Syllabic compression and speech intelligibility in hearing impaired listeners. , 1993, Scandinavian audiology. Supplementum.

[29]  Volker Hohmann,et al.  The influence of pause, attack, and decay duration of the ongoing envelope on sound lateralization. , 2015, The Journal of the Acoustical Society of America.

[30]  L. Robles,et al.  Basilar-membrane responses to tones at the base of the chinchilla cochlea. , 1997, The Journal of the Acoustical Society of America.

[31]  Skyler G. Jennings,et al.  Evaluating the effects of olivocochlear feedback on psychophysical measures of frequency selectivity. , 2012, The Journal of the Acoustical Society of America.

[32]  J Agnew,et al.  The Causes and Effects of Distortion and Internal Noise in Hearing Aids , 1998, Trends in amplification.

[33]  J. Guinan.  Olivocochlear Efferents: Anatomy, Physiology, Function, and the Measurement of Efferent Effects in Humans , 2006, Ear and hearing.

[34]  Ray Meddis,et al.  Auditory-nerve first-spike latency and auditory absolute threshold: a computer model. , 2006, The Journal of the Acoustical Society of America.

[35]  Vit Drga,et al.  Inferred basilar-membrane response functions for listeners with mild to moderate sensorineural hearing loss. , 2004, The Journal of the Acoustical Society of America.

[36]  Tim Jürgens,et al.  Hearing dummies: Individualized computer models of hearing impairment , 2014, International Journal of Audiology.

[37]  Ruth Bentler,et al.  Digital Noise Reduction: An Overview , 2006, Trends in amplification.

[38]  Tim Jürgens,et al.  Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing , 2016, International journal of audiology.

[39]  M. Liberman,et al.  Adding Insult to Injury: Cochlear Nerve Degeneration after “Temporary” Noise-Induced Hearing Loss , 2009, The Journal of Neuroscience.