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Full-Text Articles in Engineering
Multiple Fundamental Frequency Pitch Detection For Real Time Midi Applications, Nathan Hilbish
Multiple Fundamental Frequency Pitch Detection For Real Time Midi Applications, Nathan Hilbish
Theses and Dissertations
This study aimed to develop a real time multiple fundamental frequency detection algorithm for real time pitch to MIDI conversion applications. The algorithm described here uses neural network classifiers to make classifications in order to define a chord pattern (combination of multiple fundamental frequencies). The first classification uses a binary decision tree that determines the root note (first note) in a combination of notes; this is achieved through a neural network binary classifier. For each leaf of the binary tree, each classifier determines the frequency group of the root note (low or high frequency) until only two frequencies are left …
Developing A Hybrid Model To Predict Student First Year Retention And Academic Success In Stem Disciplines Using Neural Networks, Ruba Alkhasawneh
Developing A Hybrid Model To Predict Student First Year Retention And Academic Success In Stem Disciplines Using Neural Networks, Ruba Alkhasawneh
Theses and Dissertations
Understanding the reasoning behind the low enrollment and retention rates of Underrepresented Minority (URM) students (African Americans, Hispanic Americans, and Native Americans) in the disciplines of science, technology, engineering, and mathematics (STEM) has concerned many researchers for decades. Numerous studies have used traditional statistical methods to identify factors that affect and predict student retention. Recently, researchers have relied on using data mining techniques for modeling student retention in higher education [1]. This research has used neural networks for performance modeling in order to obtain an adequate understanding of factors related to first year academic success and retention of URM at …