Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 101 - 105 of 105
Description
Generative models are deep neural network-based models trained to learn the underlying distribution of a dataset. Once trained, these models can be used to sample novel data points from this distribution. Their impressive capabilities have been manifested in various generative tasks, encompassing areas like image-to-image translation, style transfer, image editing, and more. One notable application of generative models is data augmentation, aimed at expanding and diversifying the training dataset to augment the performance of deep learning models for a downstream task. Generative models can be used to create new samples similar to the original data but with different variations and properties that are difficult to capture with traditional data augmentation techniques. However, the quality, diversity, and controllability of the shape and structure of the generated samples from these models are often directly proportional to the size and diversity of the training dataset. A more extensive and diverse training dataset allows the generative model to capture overall structures present in the data and generate more diverse and realistic-looking samples.
In this dissertation, I present innovative methods designed to enhance the robustness and controllability of generative models, drawing upon physics-based, probabilistic, and geometric techniques. These methods help improve the generalization and controllability of the generative model without necessarily relying on large training datasets. I enhance the robustness of generative models by integrating classical geometric moments for shape awareness and minimizing trainable parameters. Additionally, I employ non-parametric priors for the generative model's latent space through basic probability and optimization methods to improve the fidelity of interpolated images. I adopt a hybrid approach to address domain-specific challenges with limited data and controllability, combining physics-based rendering with generative models for more realistic results.
These approaches are particularly relevant in industrial settings, where the training datasets are small and class imbalance is common. Through extensive experiments on various datasets, I demonstrate the effectiveness of the proposed methods over conventional approaches.
ContributorsSingh, Rajhans (Author) / Turaga, Pavan (Thesis advisor) / Jayasuriya, Suren (Committee member) / Berisha, Visar (Committee member) / Fazli, Pooyan (Committee member) / Arizona State University (Publisher)
Created2023
Description
This study aimed to evaluate the efficacy of Apple AirPods pro (2nd generation) Live Listen feature in enhancing word recognition and memory retention among individuals with varying degrees of hearing loss, as determined by their Signal-to-Noise Ratio (SNR) loss. Utilizing a single-group experimental design, the research measured participants' performance on word recognition and memory retention tasks with and without the Live Listen feature. Statistical analysis, including paired t-tests and linear regression, revealed significant improvements in word recognition (from 81.8% to 94.4%) and memory retention (from 43.8% to 59.4%) scores when the Live Listen feature was activated. Moreover, a positive correlation between SNR loss and recognition score improvements suggested a greater benefit for those with higher levels of hearing loss. However, the relationship with memory retention improvements was less pronounced. These findings underscore the potential of the Live Listen feature as an effective assistive listening device, highlighting its importance in enhancing auditory experiences for individuals with hearing impairments and encouraging further research into personalized auditory assistance technologies in noisy healthcare environments.
ContributorsForoogozar, Mehdi (Author) / Liss, Julie (Thesis advisor) / Berisha, Visar (Committee member) / Luo, Xin (Committee member) / Arizona State University (Publisher)
Created2024
Description
The poor spectral and temporal resolution of cochlear implants (CIs) limit their users’ music enjoyment. Remixing music by boosting vocals while attenuating spectrally complex instruments has been shown to benefit music enjoyment of postlingually deaf CI users. However, the effectiveness of music remixing in prelingually deaf CI users is still unknown. This study compared the music-remixing preferences of nine postlingually deaf, late-implanted CI users and seven prelingually deaf, early-implanted CI users, as well as their ratings of song familiarity and vocal pleasantness. Twelve songs were selected from the most streamed tracks on Spotify for testing. There were six remixed versions of each song: Original, Music-6 (6-dB attenuation of all instruments), Music-12 (12-dB attenuation of all instruments), Music-3-3-12 (3-dB attenuation of bass and drums and 12-dB attenuation of other instruments), Vocals-6 (6-dB attenuation of vocals), and Vocals-12 (12-dB attenuation of vocals). It was found that the prelingual group preferred the Music-6 and Original versions over the other versions, while the postlingual group preferred the Vocals-12 version over the Music-12 version. The prelingual group was more familiar with the songs than the postlingual group. However, the song familiarity rating did not significantly affect the patterns of preference ratings in each group. The prelingual group also had higher vocal pleasantness ratings than the postlingual group. For the prelingual group, higher vocal pleasantness led to higher preference ratings for the Music-12 version. For the postlingual group, their overall preference for the Vocals-12 version was driven by their preference ratings for songs with very unpleasant vocals. These results suggest that the patient factor of auditory experience and stimulus factor of vocal pleasantness may affect the music-remixing preferences of CI users. As such, the music-remixing strategy needs to be customized for individual patients and songs.
ContributorsVecellio, Amanda Paige (Author) / Luo, Xin (Thesis advisor) / Ringenbach, Shannon (Committee member) / Berisha, Visar (Committee member) / Zhou, Yi (Committee member) / Arizona State University (Publisher)
Created2024
Description
MyCollegeCooking.com is a student-driven initiative aimed at revolutionizing the way college students approach nutrition and cooking. Understanding the unique challenges faced by students, such as limited space and time constraints, our platform provides accessible tools and inspiration for preparing nutritious meals. Beyond offering recipes, our website includes detailed nutritional information and encourages interaction from users, fostering a dynamic community. Supported by research and feedback from over 100 college students, our focus on simplicity, accessibility, and balance addresses the common concerns of time and money. Through strategic marketing efforts, particularly leveraging social media, we aim to raise awareness and promote healthy cooking habits among college students nationwide. MyCollegeCooking.com isn't just a recipe website; it's a collaborative platform dedicated to enhancing the well-being and success of students through nutritious eating and community engagement.
ContributorsModic, Jill (Author) / Vandeest, Maren (Co-author) / Spreitzer, Nicole (Co-author) / Rennie, Isabel (Co-author) / Bryne, Jared (Thesis director) / Balven, Rachel (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor)
Created2024-05
Description
Social Loafers Bakery is an international bread bakery aimed to introduce ASU students and faculty to breads that one would likely not be able to find at their local grocery store or bakery. Tempe does not have a wide array of bread specific bakeries, and as a result, opening an international bread bakery would help differentiate us from our competition. Through the Founders Lab program, we were able to gather data through several tabling events and taste-tests to help curate a lineup of breads that out target audience would be interested in. In this report, we delve into our research, key findings, and the transformative experiences that shaped our venture during the Founders Lab program.
ContributorsKattan, Nadim (Author) / Newell, Alexandra (Co-author) / Byrne, Jared (Thesis director) / Balven, Rachel (Committee member) / Barrett, The Honors College (Contributor) / Department of Management and Entrepreneurship (Contributor)
Created2024-05