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
ii
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 Aweighted
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.