Deep learning models are typically evaluated to measure and compare their performance on a given task. The metrics that are commonly used to evaluate these models are standard metrics that are used for different tasks. In the field of music composition or generation, the standard metrics used in other fields have no clear meaning in terms of music theory. In this paper, we propose a subjective method to evaluate AI-based music composition systems by asking questions related to basic music principles to different levels of users based on their musical experience and knowledge. We use this method to compare state-of-the-art models for music composition with deep learning. We give the results of this evaluation method and we compare the responses of each user level for each evaluated model.
ABC-Eval: Benchmarking Large Language Models on Symbolic Music Understanding and Instruction Following
Graduate School of Informatics, Kyoto University, Japan（京都大学信息科学研究生院）；College of Computer Science and Artificial Intelligence, Fudan University, China（复旦大学计算机科学与人工智能学院）；Graduate School of Engineering, Kyoto University, Japan（京都大学工学研究科）
Efficient Long-Sequence Diffusion Modeling for Symbolic Music Generation
School of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Wuhan University of Technology（武汉理工大学）；School of Computer Science（计算机科学学院）；Hubei University of Technology（湖北工业大学）；School of Information Engineering（信息工程学院）；Hubei University of Economics（湖北经济学院）；Hubei Key Laboratory of Digital Finance Innovation（湖北省数字金融创新重点实验室）
BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps
South China University of Technology（南方科技大学）；National University of Singapore（新加坡国立大学）；Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；New York University（纽约大学）
BeatEdit: Symbolic Music Generation as Explicit Editing
School of Future Technology South China University of Technology Guangzhou China（未来技术学院 华南理工大学 广州 中国）；South China University of Technology（华南理工大学）；Nanjing University（南京大学）
NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms
Central Conservatory of Music（中央音乐学院）；University of Rochester（罗切斯特大学）；Beijing Flowingtech Ltd.（北京流音科技有限公司）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）
Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey
Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多综合理工学院）；Singapore University of Technology and Design（新加坡科技设计大学）
Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation
Artificial Intelligence Lab, Otto-von-Guericke-University, Magdeburg, Germany（奥托·冯·格里克大学人工智能实验室）；University of Porto, Faculty of Engineering and INESC TEC（波尔图大学工程学院）
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
Shanghai Jiao Tong University（上海交通大学）；Microsoft Research Asia（微软亚洲研究院）；Nanjing University（南京大学）；National Engineering Research Center for Software Engineering, Peking University（北京大学软件工程国家工程研究中心）
Multi-view MidiVAE: Fusing Track- and Bar-view Representations for Long Multi-track Symbolic Music Generation
Tsinghua University（清华大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Skywork AI PTE. LTD.（昆仑万维人工智能（Skywork AI））；The Chinese University of Hong Kong（香港中文大学）
Generating High-quality Symbolic Music Using Fine-grained Discriminators
Hangzhou Dianzi University（杭州电子科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Xi’an Jiaotong University（西安交通大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Macquarie University（麦考瑞大学）
MuPT: A Generative Symbolic Music Pretrained Transformer
M-A-P；HKUST（香港科技大学）；University of Manchester（曼彻斯特大学）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；QMUL（伦敦玛丽女王大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；MILA（米拉人工智能研究所）；Institute of Automation, CAS（中国科学院自动化研究所）；Central Conservatory of Music（中央音乐学院）；PKU（北京大学）；SJTU（上海交通大学）；MSRA（微软研究院）；University of Waterloo（滑铁卢大学）；Vector Institue（向量研究所）
