Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the explosive rise of AI-generated music platforms has created an entirely new and largely unexplored landscape, where a surge of songs is produced and consumed daily without the traditional markers of artist reputation or label backing. Key, yet unexplored in this pursuit is aesthetic quality. We propose APEX, the first large-scale multi-task learning framework for AI-generated music, trained on over 211k songs (10k hours of audio) from Suno and Udio, that jointly predicts engagement-based popularity signals - streams and likes scores - alongside five perceptual aesthetic quality dimensions from frozen audio embeddings extracted from MERT, a self-supervised music understanding model. Aesthetic quality and popularity capture complementary aspects of music that together prove valuable: in an out-of-distribution evaluation on the Music Arena dataset, comprising pairwise human preference battles across eleven generative music systems unseen during training, including aesthetic features consistently improves preference prediction, demonstrating strong generalisation of the learned representations across generative architectures.
Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work …
An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance
Beijing Electronic Science and Technology Institute（北京电子科技学院）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Tongji University（同济大学）；Tianjin Conservatory of Music（天津音乐学院）
MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations
Central Conservatory of Music, China（中央音乐学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Tianjin Conservatory of Music（天津音乐学院）；Beijing Electronic Science and Technology Institute（北京电子科技研究所）；Peking University（北京大学）
Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China（计算机科学与技术学院，西安交通大学，西安，中国）；Central Media Technology Institute, Huawei（中央媒体技术研究院，华为）
Modern digital music production typically involves combining numerous acoustic elements to compile a piece of music. Important types of such elements are drum samples, which determine the characteristics of the percussive components of the piece. Artists must use their aesthetic judgement to assess whether a given drum sample fits the current musical context. However, selecting drum samples from a potentially large library is tedious and may interrupt the creative flow. In this work, we explore the automatic drum sample retrieval based on aesthetic principles learned from data. As a result, ar…
Deep learning for music generation. Four approaches and their comparative evaluation
Faculty of Automatics and Computer Science, University POLITEHNICA of Bucharest（布加勒斯特理工大学自动控制与计算机科学学院）；Romanian Academy Research Institute for Artificial Intelligence（罗马尼亚科学院人工智能研究所）；Academy of Romanian Scientists（罗马尼亚科学院）
Ocean University of China（中国海洋大学）；Xi’an Jiaotong University（西安交通大学）
In this paper, we propose SinTra, an auto-regressive sequential generative model that can learn from a single multi-track music segment, to generate coherent, aesthetic, and variable polyphonic music of multi-instruments with an arbitrary length of bar. For this task, to ensure the relevance of generated samples and training music, we present a novel pitch-group representation. SinTra, consisting of a pyramid of Transformer-XL with a multi-scale training strategy, can learn both the musical structure and the relative positional relationship between notes of the single training music segment. A…
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）
From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn's "The Lark" Integrating Electroacoustic Analysis
Shenyang Conservatory of Music（沈阳音乐学院）；Education Information Center, Shenyang Conservatory of Music（沈阳音乐学院教育信息中心）
Improving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings
Research Center for Information Technology Innovation, Academia Sinica（学术院信息科技创新研究中心）；College of Informatics, National Chengchi University（国立成功大学资讯学院）；Institute of Information Science, Academia Sinica（学术院信息科学研究所）；Institute of Communications Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学通讯工程研究所）
Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generation
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information（广东省计算智能与网络空间信息重点实验室）；The Hong Kong Polytechnic University（香港理工大学）；Singapore Management University（新加坡管理大学）
