Literature DB >> 28054911

Evaluation of a Wind Noise Attenuation Algorithm on Subjective Annoyance and Speech-in-Wind Performance.

Petri Korhonen1, Francis Kuk1, Eric Seper1, Martin Mørkebjerg2, Majken Roikjer2.   

Abstract

BACKGROUND: Wind noise is a common problem reported by hearing aid wearers. The MarkeTrak VIII reported that 42% of hearing aid wearers are not satisfied with the performance of their hearing aids in situations where wind is present.
PURPOSE: The current study investigated the effect of a new wind noise attenuation (WNA) algorithm on subjective annoyance and speech recognition in the presence of wind. RESEARCH
DESIGN: A single-blinded, repeated measures design was used. STUDY SAMPLE: Fifteen experienced hearing aid wearers with bilaterally symmetrical (≤10 dB) mild-to-moderate sensorineural hearing loss participated in the study. DATA COLLECTION AND ANALYSIS: Subjective rating for wind noise annoyance was measured for wind presented alone from 0° and 290° at wind speeds of 4, 5, 6, 7, and 10 m/sec. Phoneme identification performance was measured using Widex Office of Clinical Amplification Nonsense Syllable Test presented at 60, 65, 70, and 75 dB SPL from 270° in the presence of wind originating from 0° at a speed of 5 m/sec.
RESULTS: The subjective annoyance from wind noise was reduced for wind originating from 0° at wind speeds from 4 to 7 m/sec. The largest improvement in phoneme identification with the WNA algorithm was 48.2% when speech was presented from 270° at 65 dB SPL and the wind originated from 0° azimuth at 5 m/sec.
CONCLUSION: The WNA algorithm used in this study reduced subjective annoyance for wind speeds ranging from 4 to 7 m/sec. The algorithm was effective in improving speech identification in the presence of wind originating from 0° at 5 m/sec. These results suggest that the WNA algorithm used in the current study could expand the range of real-life situations where a hearing-impaired person can use the hearing aid optimally. American Academy of Audiology

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Year:  2017        PMID: 28054911     DOI: 10.3766/jaaa.15135

Source DB:  PubMed          Journal:  J Am Acad Audiol        ISSN: 1050-0545            Impact factor:   1.664


  3 in total

1.  Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Authors:  Mahmoud Keshavarzi; Tobias Goehring; Justin Zakis; Richard E Turner; Brian C J Moore
Journal:  Trends Hear       Date:  2018 Jan-Dec       Impact factor: 3.293

2.  A Comparison of Environment Classification Among Premium Hearing Instruments.

Authors:  Anusha Yellamsetty; Erol J Ozmeral; Robert A Budinsky; David A Eddins
Journal:  Trends Hear       Date:  2021 Jan-Dec       Impact factor: 3.293

3.  Efficacy and Effectiveness of Advanced Hearing Aid Directional and Noise Reduction Technologies for Older Adults With Mild to Moderate Hearing Loss.

Authors:  Yu-Hsiang Wu; Elizabeth Stangl; Octav Chipara; Syed Shabih Hasan; Sean DeVries; Jacob Oleson
Journal:  Ear Hear       Date:  2019 Jul/Aug       Impact factor: 3.570

  3 in total

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