Matching Items (6)
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- All Subjects: Signal Processing
- Creators: Weinhold, Juliet
- Creators: Richmond, Christ
Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
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
Description
Research on /r/ production previously used formant analysis as the primary acoustic analysis, with particular focus on the low third formant in the speech signal. Prior imaging of speech used X-Ray, MRI, and electromagnetic midsagittal articulometer systems. More recently, the signal processing technique of Mel-log spectral plots has been used to study /r/ production in children and female adults. Ultrasound imaging of the tongue also has been used to image the tongue during speech production in both clinical and research settings. The current study attempts to describe /r/ production in three different allophonic contexts; vocalic, prevocalic, and postvocalic positions. Ultrasound analysis, formant analysis, Mel-log spectral plots, and /r/ duration were measured for /r/ production in 29 adult speakers (10 male, 19 female). A possible relationship between these variables was also explored. Results showed that the amount of superior constriction in the postvocalic /r/ allophone was significantly lower than the other /r/ allophones. Formant two was significantly lower and the distance between formant two and three was significantly higher for the prevocalic /r/ allophone. Vocalic /r/ had the longest average duration, while prevocalic /r/ had the shortest duration. Signal processing results revealed candidate Mel-bin values for accurate /r/ production for each allophone of /r/. The results indicate that allophones of /r/ can be distinguished based the different analyses. However, relationships between these analyses are still unclear. Future research is needed in order to gather more data on /r/ acoustics and articulation in order to find possible relationships between the analyses for /r/ production.
ContributorsHirsch, Megan Elizabeth (Author) / Weinhold, Juliet (Thesis director) / Gardner, Joshua (Committee member) / Department of Speech and Hearing Science (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The purpose of this study was to identify acoustic markers that correlate with accurate and inaccurate /r/ production in children ages 5-8 using signal processing. In addition, the researcher aimed to identify predictive acoustic markers that relate to changes in /r/ accuracy. A total of 35 children (23 accurate, 12 inaccurate, 8 longitudinal) were recorded. Computerized stimuli were presented on a PC laptop computer and the children were asked to do five tasks to elicit spontaneous and imitated /r/ production in all positions. Files were edited and analyzed using a filter bank approach centered at 40 frequencies based on the Mel-scale. T-tests were used to compare spectral energy of tokens between accurate and inaccurate groups and additional t-tests were used to compare duration of accurate and inaccurate files. Results included significant differences between the accurate and inaccurate productions of /r/, notable differences in the 24-26 mel bin range, and longer duration of inaccurate /r/ than accurate. Signal processing successfully identified acoustic features of accurate and inaccurate production of /r/ and candidate predictive markers that may be associated with acquisition of /r/.
ContributorsBecvar, Brittany Patricia (Author) / Azuma, Tamiko (Thesis advisor) / Weinhold, Juliet (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2017
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 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
Description
Power spectral analysis is a fundamental aspect of signal processing used in the detection and \\estimation of various signal features. Signals spaced closely in frequency are problematic and lead analysts to miss crucial details surrounding the data. The Capon and Bartlett methods are non-parametric filterbank approaches to power spectrum estimation. The Capon algorithm is known as the "adaptive" approach to power spectrum estimation because its filter impulse responses are adapted to fit the characteristics of the data. The Bartlett method is known as the "conventional" approach to power spectrum estimation (PSE) and has a fixed deterministic filter. Both techniques rely on the Sample Covariance Matrix (SCM). The first objective of this project is to analyze the origins and characteristics of the Capon and Bartlett methods to understand their abilities to resolve signals closely spaced in frequency. Taking into consideration the Capon and Bartlett's reliance on the SCM, there is a novelty in combining these two algorithms using their cross-coherence. The second objective of this project is to analyze the performance of the Capon-Bartlett Cross Spectra. This study will involve Matlab simulations of known test cases and comparisons with approximate theoretical predictions.
ContributorsYoshiyama, Cassidy (Author) / Richmond, Christ (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05