Music visualization with deep learning

Music visualization offers a unique way to experience music beyond just listening. While dynamic visualizations are the status quo, our research has found a capacity for static visualizations to convey complex musical concepts. Moreover, with the advent of advancements in artificial intelligence and...

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Main Author: Kumar, Neel
Other Authors: Alexei Sourin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176030
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1760302024-05-17T15:38:10Z Music visualization with deep learning Kumar, Neel Alexei Sourin School of Computer Science and Engineering assourin@ntu.edu.sg Computer and Information Science Deep learning Neural networks Music visualization Stable diffusion Music visualization offers a unique way to experience music beyond just listening. While dynamic visualizations are the status quo, our research has found a capacity for static visualizations to convey complex musical concepts. Moreover, with the advent of advancements in artificial intelligence and deep learning making it easier than ever to generate visualizations through technologies like DALL.E and Stable Diffusion, this study investigates its potential for generating static abstract visualizations of music, aiming to represent higher-level features such as mode, timbre, and symbolism. By leveraging technical advancements, particularly in transformer-based neural networks, this study explores a novel approach that combines music and natural language processing to create visual signatures that reflect the essence and emotional content of musical compositions. The findings demonstrate the model's capability to produce visually compelling and aesthetically pleasing representations of music, highlighting the underutilized potential of static visualizations in capturing complex musical attributes as well as identifying scopes for future improvement. Finally, the effectiveness of this approach was evaluated to test the hypothesis and usefulness of results. Several practical applications for visualizations such as enhancements to live and recorded performances, educational tools, therapeutic aids and artistic entertainment amongst others. While the results show promise, they underscore the need for refinement and further exploration to fully unlock the potential of this technology. Ultimately, the ability of this technology to create cross-modal understanding—capturing both general patterns and nuanced details—will determine their effectiveness in reshaping the intersection of audio and visual experiences. Bachelor's degree 2024-05-13T05:50:24Z 2024-05-13T05:50:24Z 2024 Final Year Project (FYP) Kumar, N. (2024). Music visualization with deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176030 https://hdl.handle.net/10356/176030 en application/pdf application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Deep learning
Neural networks
Music visualization
Stable diffusion
spellingShingle Computer and Information Science
Deep learning
Neural networks
Music visualization
Stable diffusion
Kumar, Neel
Music visualization with deep learning
description Music visualization offers a unique way to experience music beyond just listening. While dynamic visualizations are the status quo, our research has found a capacity for static visualizations to convey complex musical concepts. Moreover, with the advent of advancements in artificial intelligence and deep learning making it easier than ever to generate visualizations through technologies like DALL.E and Stable Diffusion, this study investigates its potential for generating static abstract visualizations of music, aiming to represent higher-level features such as mode, timbre, and symbolism. By leveraging technical advancements, particularly in transformer-based neural networks, this study explores a novel approach that combines music and natural language processing to create visual signatures that reflect the essence and emotional content of musical compositions. The findings demonstrate the model's capability to produce visually compelling and aesthetically pleasing representations of music, highlighting the underutilized potential of static visualizations in capturing complex musical attributes as well as identifying scopes for future improvement. Finally, the effectiveness of this approach was evaluated to test the hypothesis and usefulness of results. Several practical applications for visualizations such as enhancements to live and recorded performances, educational tools, therapeutic aids and artistic entertainment amongst others. While the results show promise, they underscore the need for refinement and further exploration to fully unlock the potential of this technology. Ultimately, the ability of this technology to create cross-modal understanding—capturing both general patterns and nuanced details—will determine their effectiveness in reshaping the intersection of audio and visual experiences.
author2 Alexei Sourin
author_facet Alexei Sourin
Kumar, Neel
format Final Year Project
author Kumar, Neel
author_sort Kumar, Neel
title Music visualization with deep learning
title_short Music visualization with deep learning
title_full Music visualization with deep learning
title_fullStr Music visualization with deep learning
title_full_unstemmed Music visualization with deep learning
title_sort music visualization with deep learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176030
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