Development and Validation of New Models and Metrics for the Assessments of Noise-Induced Hearing Loss
[Thesis]
Al-Dayyeni, Wisam Subhi Talib
Qin, Jun
Southern Illinois University at Carbondale
2019
126
Ph.D.
Southern Illinois University at Carbondale
2019
Noise-induced hearing loss (NIHL) is one of the most common illnesses that is frequently reported in the occupational and military sectors. Hearing loss due to high noise exposure is a major health problem with economic consequences. Industrial and military noise exposures often contain high-level impulsive noise components. The presence of these impulsive noise components complicates the assessment of noise levels for hearing conservation purposes. The current noise guidelines use equal energy hypothesis (EEH) based metrics to evaluate the risk of hearing loss. A number of studies show that the current noise metrics often underestimates the risk of hearing loss in high-level complex noise environments. The overarching goal of this dissertation is to develop advance signal processing based methods for more accurate assessments of the risk of NIHL. For these assessments, various auditory filters that take into account the physiological characteristics of the ear are used. These filters will help to understand the complexity of the ear's response to high-level complex noises. In this study, the F-weighting and the fatigue model are evaluated using animal noise exposure data. The results show that both the F-weighting and the fatigue model demonstrate better correlations with the hearing loss indicators compared with conventional noise metrics. Also, the dual resonance nonlinear filter and the rounded-exponential filter are applied to develop the velocity excitation pattern and the loudness excitation pattern. The results show that both excitation patterns can potentially be used as noise hazardous level indexes for the assessment of NIHL. Moreover, six noise metrics derived from six different auditory models are developed based on excitation patterns to assess NIHL. The designed noise metrics are evaluated by their correlations with chinchilla noise exposure data. The results show that the developed metrics have better correlation with hearing loss assessment compared to conventional metrics.