Controlling the Emotion of Music using Generative Deep Learning

该研究项目旨在开发新颖的、基于知识的深度学习技术，用于情感音乐的生成。主要目标是：(1)探索将“香草”旋律转换为表达特定情绪状态的有效方法；(2)在统计分析、训练神经网络、音乐信息检索（MIR）或音乐理论中确定哪种技术对生成的旋律提供了最佳的情绪控制。这种方法将包括重新采样由现有模型生成的旋律，一个音符一个音符地反映所选择的情绪。每一种技术的有效性将使用小的符号音乐数据集进行经验评估。将创建一个原型，允许用户生成具有指定情绪的新旋律或将现有旋律转换为所选的情绪状态。该项目在将知识增强的深度学习方法应用于音乐创作以表达情感方面具有新颖性。它的贡献将扩展基于知识的深度学习技术的知识，并强调工程学、物理科学和音乐学的交叉。

This research project aims to develop novel, knowledge-based deep learning techniques for emotional music generation. The primary objectives are: (1) To explore effective methods for converting 'vanilla' melodies into ones that express a specific emotional state, and (2) To identify which technique among statistical analysis, training neural networks, music information retrieval (MIR), or music theory offers the best emotion control over generated melodies.The approach will involve resampling a melody, generated by an existing model, note-by-note, to reflect the chosen emotion. The effectiveness of each technique will be empirically evaluated using small datasets of symbolic music. A prototype will be created to allow users to either generate a new melody with a specified emotion or convert an existing melody to the chosen emotional state.This project holds novelty in applying knowledge-enhanced deep learning methods to music composition for emotion representation. Its contributions will expand the knowledge in knowledge-based deep learning techniques and underscore the intersection of engineering, physical sciences, and musicology.