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Edge Detection from Spectral Phase Data

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The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor…

The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Predicting /r/ Acquisition: A Longitudinal Analysis Using Signal Processing

Description

The purpose of this longitudinal study was to predict /r/ acquisition using acoustic signal processing. 19 children, aged 5-7 with inaccurate /r/, were followed until they turned 8 or acquired /r/, whichever came first. Acoustic and descriptive data from 14 participants were analyzed. The remaining 5 children continued to be…

The purpose of this longitudinal study was to predict /r/ acquisition using acoustic signal processing. 19 children, aged 5-7 with inaccurate /r/, were followed until they turned 8 or acquired /r/, whichever came first. Acoustic and descriptive data from 14 participants were analyzed. The remaining 5 children continued to be followed. The study analyzed differences in spectral energy at the baseline acoustic signals of participants who eventually acquired /r/ compared to that of those who did not acquire /r/. Results indicated significant differences between groups in the baseline signals for vocalic and postvocalic /r/, suggesting that the acquisition of certain allophones may be predictable. Participants’ articulatory changes made during the progression of acquisition were also analyzed spectrally. A retrospective analysis described the pattern in which /r/ allophones were acquired, proposing that vocalic /r/ and the postvocalic variant of consonantal /r/ may be acquired prior to prevocalic /r/, and /r/ followed by low vowels may be acquired before /r/ followed by high vowels, although individual variations exist.
ContributorsConger, Sarah Grace (Author) / Weinhold, Juliet (Thesis director) / Daliri, Ayoub (Committee member) / Bruce, Laurel (Committee member) / College of Health Solutions (Contributor, Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05