Emotion-driven melody harmonization aims to generate diverse harmonies for a single melody to convey desired emotions. Previous research found it hard to alter the perceived emotional valence of lead sheets only by harmonizing the same melody with different chords, which may be attributed to the constraints imposed by the melody itself and the limitation of existing music representation. In this paper, we propose a novel functional representation for symbolic music. This new method takes musical keys into account, recognizing their significant role in shaping music's emotional character through major-minor tonality. It also allows for melodic variation with respect to keys and addresses the problem of data scarcity for better emotion modeling. A Transformer is employed to harmonize key-adaptable melodies, allowing for keys determined in rule-based or model-based manner. Experimental results confirm the effectiveness of our new representation in generating key-aware harmonies, with objective and subjective evaluations affirming the potential of our approach to convey specific valence for versatile melody.
Pay (Cross) Attention to the Melody: Curriculum Masking for Single-Encoder Melodic Harmonization
Department of Music Technology（音乐技术系）；Acoustics Hellenic Mediterranean University, Greece Archimedes, Athena RC, Greece（声学 希腊地中海大学 阿里卡斯萨斯）；Institute of Language（语言研究所）；School of Music Studies Aristotle University of Thessaloniki, Greece（音乐研究学院 阿里斯托芬大学）
Incorporating Structure and Chord Constraints in Symbolic Transformer-based Melodic Harmonization
Department of Music Technology and Acoustics（音乐科技与声学系）；Hellenic Mediterranean University（希腊地中海大学）；Archimedes, Athena RC（阿基米德，雅典RC）；Institute of Language and Speech Processing and Archimedes, Athena RC（语言与语音处理研究所及阿基米德，雅典RC）；School of Music Studies（音乐研究学院）
WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Innovation Center of Yangtze River Delta（长三角创新中心）；Microsoft Research Asia（微软亚洲研究院）
MeloDISinger: Melody-Aware & Duration-Preserving Singing Voice Editing with Audio Infilling
Graduate School of Artificial Intelligence, KAIST, South Korea（韩国釜山科学技术院人工智能研究生院）；Graduate School of Culture Technology, KAIST, South Korea（韩国釜山科学技术院文化科技研究生院）
SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours
Academia Sinica（中央研究院）
歌曲的重复性是决定听众是否喜欢它的一个基本的主观因素，在信息论的帮助下，它可以被描述为一个马尔可夫链模型的音乐序列的转移概率。在这项研究中，我们引入的概念，推导熵随时间的变化，使每个和弦序列的惊喜轮廓可以提取。在此基础上，我们提出了一个用户可控的框架，使用条件变分自编码器（CVAE）协调的旋律的基础上给定的和弦惊喜指示。通过显式条件，该模型可以随机生成各种和声和弦进行旋律，斯皮尔曼的相关性和p值的显着性表明，所得到的和弦进行匹配给定的惊喜轮廓相当好。香草CVAE模型进行了评估，在一个基本的旋律协调任务（无惊喜控制）的六个客观指标。在Hooktheory Lead Sheet数据集上的实验结果表明，我们的模型达到了与最先进的旋律协调模型相当的性能。
Automatic Melody Harmonization with Triad Chords: A Comparative Study
Academia Sinica（中央研究院）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Nihon University（日本大学）；Georgia Institute of Technology（佐治亚理工学院）；KKBOX Inc.（KKBOX公司）
The MPB Corpus: A Dataset of Melody, Rhythm, Harmony, and Melody-Harmony Relationships in Brazilian Popular Music
School of Music, Federal University of Rio de Janeiro（里约热内卢联邦大学音乐学院）；Institute of Mathematics, Federal University of Rio de Janeiro（里约热内卢联邦大学数学研究所）；Institut für Theorie und Geschichte, Anton Bruckner Universität（安东布鲁克纳大学理论与历史研究所）
Evolutionary modelling reveals melodic and harmonic constraints on global scale structure
Department of Physics, Ulsan National Institute of Science and Technology（物理系，乌山国立科学技术研究所）；McMaster University（麦马斯特大学）；University of Auckland（奥克兰大学）；Keio University（庆应大学）
University of Cambridge, UK（剑桥大学，英国）；RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway（节奏、时间与运动跨学科研究中心，奥斯陆大学，挪威）；Department of Psychology, Goldsmiths College, University of London, UK（心理学系，伦敦大学金史密斯学院，英国）；Department of Life Sciences, Leipzig University, Germany（生命科学系，莱比锡大学，德国）；Division of Social Science, New York University Abu Dhabi, UAE（社会科学系，阿布扎赫尔新 york 大学，阿联酋）；Department of Psychology, Cornell University, USA（心理学系，康奈尔大学，美国）
Open-Source Intelligence and Music Information Retrieval for Geographic Attribution of Musical Affect and the Ecological Limits of Population Inference
Information and motor constraints shape melodic diversity across cultures
Center for Algorithmic；Robotized Synthesis, Institute for Basic Science, Ulsan 44919, South Korea；School of Business Administration, Ulsan National Institute of Science；Department of Molecular Life Science, Tokai University School of Medicine, Kanagawa, Japan；Department of Biomedical Engineering, Ulsan National Institute of Science；Cognitive Science Research Group, School of Electronic Engineering \& Computer Science, Queen Mary University of London, London, United Kingdom；Department of Clinical Medicine, Aarhus University, Aarhus, Denmark；Center for Soft；Living Matter, Institute for Basic Science, Ulsan 44919, South Korea；Departments of Physics；Chemistry, Ulsan National Institute of Science
M6(GPT)3: Generating Multitrack Modifiable Multi-Minute MIDI Music from Text using Genetic algorithms, Probabilistic methods and GPT Models in any Progression and Time Signature
Institute of Computer Science（计算机科学研究院）；Warsaw University of Technology（华沙技术大学）；Department of Robotics and Decision Systems（机器人与决策系统系）；Gdansk University of Technology（格但斯克技术大学）
