Transient Noise Reduction in Cochlear Implant Users: a Multi-Band Approach

A previously-tested transient noise reduction (TNR) algorithm for cochlear implant (CI) users was modified to detect and attenuate transients independently across multiple frequency-bands. Since speech and transient noise are often spectrally distinct, we hypothesized that benefits in speech intelligibility can be achieved over the earlier single-band design. Fifteen experienced CI users (49 to 72 years) were tested unilaterally using pre-processed stimuli delivered directly to a speech processor. Speech intelligibility in transient and soft stationary noise, subjective sound quality and the recognition of warning signals was investigated in three processing conditions: no TNR (TNRoff), single-band TNR (TNRsgl) and multi-band TNR (TNRmult). Notably, TNRmult improved speech reception thresholds (SRTs) in cafeteria noise and office noise by up to 3 dB over both TNRoff and TNRsgl, and yielded higher comfort and clarity ratings in cafeteria noise. Our results indicate that multi-band transient noise reduction may be advantageous compared to a single-band approach, and reveal a substantial overall potential for TNR to improve speech perception and listening comfort in CI users.

[1]  B Kollmeier,et al.  Development and evaluation of a German sentence test for objective and subjective speech intelligibility assessment. , 1997, The Journal of the Acoustical Society of America.

[2]  Birger Kollmeier,et al.  Development and analysis of an International Speech Test Signal (ISTS) , 2010, International journal of audiology.

[3]  Thomas Lenarz,et al.  Evaluation of a Transient Noise Reduction Algorithm in Cochlear Implant Users , 2015, Audiology research.

[4]  Francis Kuk,et al.  Effects of a transient noise reduction algorithm on speech understanding, subjective preference, and preferred gain. , 2013, Journal of the American Academy of Audiology.

[5]  Ruth A. Bentler,et al.  Evaluation of a transient noise reduction strategy for hearing AIDS. , 2012, Journal of the American Academy of Audiology.

[6]  D Henderson,et al.  Impulse noise: critical review. , 1986, The Journal of the Acoustical Society of America.

[7]  Israel Cohen,et al.  Transient Interference Suppression in Speech Signals Based on the OM-LSA Algorithm , 2012, IWAENC.

[8]  I. Hochmair-Desoyer,et al.  The HSM sentence test as a tool for evaluating the speech understanding in noise of cochlear implant users. , 1997, The American journal of otology.

[9]  David J. Groggel,et al.  Practical Nonparametric Statistics , 2000, Technometrics.

[10]  G. E. Peterson,et al.  Control Methods Used in a Study of the Vowels , 1951 .

[11]  Thomas A. Powers,et al.  New algorithm is designed to take the annoyance out of transient noise , 2007 .

[12]  Thomas Lenarz,et al.  Results of a Pilot Study With a Signal Enhancement Algorithm for HiRes 120 Cochlear Implant Users , 2010, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[13]  Jeffrey J DiGiovanni,et al.  Effects of transient noise reduction algorithms on speech intelligibility and ratings of hearing aid users. , 2011, American journal of audiology.

[14]  Thomas Lenarz,et al.  Evaluation of the Harmony Soundprocessor in Combination With the Speech Coding Strategy HiRes 120 , 2008, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[15]  Andreas Büchner,et al.  Comparison of dual-time-constant and fast-acting automatic gain control (AGC) systems in cochlear implants , 2009, International journal of audiology.

[16]  Gitte Keidser,et al.  Evaluation of a noise‐reduction algorithm that targets non‐speech transient sounds , 2007 .

[17]  Thomas Lenarz,et al.  Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions , 2014, PloS one.