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)探索有效的方法，将“香草”旋律转换成表达特定情绪状态的旋律；(2)确定统计分析、训练神经网络、音乐信息检索(MIR)或音乐理论中哪种技术对生成的旋律提供最好的情绪控制。该方法将涉及对由现有模型生成的旋律逐个音符进行重采样，以反映所选择的情感。每种技术的有效性将通过使用符号音乐的小数据集进行经验性评估。将创建一个原型，允许用户生成具有特定情感的新旋律，或将现有旋律转换为选定的情感状态。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。它的贡献将扩大基于知识的深度学习技术方面的知识，并强调工程学、物理学和音乐学的交叉。

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.