Sequence generation can be a difficult problem to solve using strictly heuristic approaches. To decide on the next value in a series of values requires a level of understanding that must be unique to a given series, and this is challenging to represent in a finite feature space. Using Machine Learning principles, researchers have developed strategies for modeling sequence prediction behavior, which can be applied to a wide range of data sets including shopping trends, predictive text, and music composition. As with any potential solution to a problem that utilizes Machine Learning techniques, deciding which features of input values are statically bound to desirable output is only one of many concerns. In the case of music composition, elements of music such as phrase, key, tempo, melody, accompaniment, and various forms of modulation must also play a part in giving a piece its overall quality and allows the listener to perceive its musical language. This project, DebuSequencer, will explore the use of pitch, octave, dynamics, tempo, and key from MIDI instruction data as features to train a pair of Recurrent Neural Networks connected as a Generative Adversarial Network. The methods of feature selection and network configuration formulated within this project will generate sequences in the style of various types of music.
|Commitee:||Bischoff, John L., Oparah, Chinyere|
|Department:||Interdisciplinary Computer Science|
|School Location:||United States -- California|
|Source:||MAI 81/2(E), Masters Abstracts International|
|Subjects:||Artificial intelligence, Computer science, Musical composition|
|Keywords:||GAN, Keras, LSTM, Machine learning, Music composition, RNN|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be