Development and applications of analytic wavelet transform technique with special attention to noise risk assessment of impulsive noises .

摘要

The development of the Analytical Wavelet Transform (AWT) as a transient signal analysis tool and the development of new noise metrics for possible future noise guidelines are the two major contributions of this dissertation research.;The AWT is developed and validated as a tool for transient signal analysis. Underlying theories and basic properties of the AWT are discussed in comparison with a commonly used short-time Fourier transform (STFT) method. The AWT is set up specifically for applications to noise and vibration analysis and applied to characterize highly impulsive sound and vibration signals. A new concept, 1/3 octave time history is defined and applied to the risk assessment of impulsive noise induced hearing loss. AWT is also applied to assess the performance of hearing protectors, to calculate the reverberation time of a room, and to characterize vibration signals of hand-tools. Some new concepts are developed taking the advantage of the capability of the AWT, which are time-frequency (T-F) and time-averaged noise reduction (NR) and frequency weighted time history.;The AWT is applied as a main signal processing tool to develop new noise metrics to assess the risk of impulsive noise induced hearing loss. Noise-induced hearing loss (NIHL) is the most common job-related illness. Current noise guidelines recommend the allowed noise exposure based on the equal energy hypothesis (EEH). The EEH based approach is appropriate for steady-state noises but not for impulsive noises because the time-averaging effect significantly underestimates the exposure risk. Because the A-weighted sound pressure level (SPL), a single valued metric, is used in current noise guidelines, risks of noises of vastly different temporal or spectral characteristic cannot be distinguished if their SPL levels are the same. To identify new noise metrics, fourteen new metric designs are developed which reflect the T-F characteristics of the noise obtained by the AWT in uniquely different ways. Statistical correlations of the metrics with the NIHL observed in a chinchilla noise exposure test are used to identify the best metric. The study identifies a few promising new noise metrics, which may be used to develop an improved noise guideline.