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Title: Music generation with deep learning techniques
Authors: Toh, Raymond Kwan How
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Visual arts and music::Music::Compositions
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Toh, R. K. H. (2021). Music generation with deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE20-0007
Abstract: This report demonstrated the use of a deep convolutional generative adversarial network (DCGAN) in generating expressive music with dynamics. The existing deep learning models for music generation were reviewed. However, most research focused on musical composition and removed expressive attributes during data preprocessing, which resulted in mechanical-sounding, generated music. To address the issue, music elements such as pitch, time, velocity were extracted from MIDI files and encoded with piano roll data representation. With the piano roll data representation, DCGAN learned the data distribution from the given dataset and generated new data derived from the same distribution. The generated music was evaluated based on its incorporation of music dynamics and a user study. The evaluation results verified that DCGAN was capable of generating expressive music comprising of music dynamics and syncopated rhythm.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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