MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computer Science, Hubei University of Technology（湖北工业大学计算机学院）
In recent years, deep learning has significantly advanced the MIDI domain, solidifying music generation as a key application of artificial intelligence. However, existing research primarily focuses on Western music and encounters challenges in generating melodies for Chinese traditional music, especially in capturing modal characteristics and emotional expression. To address these issues, we propose a new architecture, the Dual-Feature Modeling Module, which integrates the long-range dependency modeling of the Mamba Block with the global structure capturing capabilities of the Transformer Block. Additionally, we introduce the Bidirectional Mamba Fusion Layer, which integrates local details and global structures through bidirectional scanning, enhancing the modeling of complex sequences. Building on this architecture, we propose the REMI-M representation, which more accurately captures and generates modal information in melodies. To support this research, we developed FolkDB, a high-quality Chinese traditional music dataset encompassing various styles and totaling over 11 hours of music. Experimental results demonstrate that the proposed architecture excels in generating melodies with Chinese traditional music characteristics, offering a new and effective solution for music generation.
Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example
Peking University（北京大学）；Wangxuan Institute of Computer Technology, PKU（北京大学王选计算机研究所）；China Conservatory of Music（中国音乐学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Innovation Center of Yangtze River Delta, Zhejiang University（浙江大学长三角创新中心）
Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio
Department of Artificial Intelligence, Sogang University（西江大学人工智能系）；Sogang Future Lab, Sogang University（西江大学未来实验室）；Department of Physics Education, Seoul National University（首尔大学物理教育系）；Computer Science Department, Carnegie Mellon University（卡内基梅隆大学计算机科学系）；Department of Art & Technology, Sogang University（西江大学艺术与技术系）
Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Edinburgh（爱丁堡大学）；Meitu Inc（美图公司）；The Chinese University of Hong Kong（香港中文大学）；Beihang University（北京航空航天大学）
Art2Mus: Artwork-to-Music Generation via Visual Conditioning and Large-Scale Cross-Modal Alignment
University of Bari Aldo Moro（巴里大学阿尔多·莫罗大学）；Catalonia's Telecommunications Technology Centre（加泰罗尼亚电信技术中心）；University of Picardie Jules Verne（皮卡第大学朱利·瓦内大学）
Zero-Effort Image-to-Music Generation: An Interpretable RAG-based VLM Approach
The Hong Kong University of Science and Technology（香港科学与技术大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）；The Hong Kong University of Science（香港科学大学）；The Hong Kong Polytechnic University Hong Kong China（香港理工大学香港中国）；The University of Hong Kong Hong Kong China（香港大学香港中国）
KuiSCIMA v2.0: Improved Baselines, Calibration, and Cross-Notation Generalization for Historical Chinese Music Notations in Jiang Kui's Baishidaoren Gequ
Graz University of Technology（格拉茨技术大学）；Know Center Research GmbH（Know Center 研究有限公司）
A Survey on Cross-Modal Interaction Between Music and Multimodal Data
MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. E-mail: S. Li；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China. E-mail: F. Shen；Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. E-mail: .
Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics
University of Copenhagen（哥本哈根大学）
本文旨在测试多模态方法的音乐情感识别（MER）是否比单模态的高层次的歌曲特征和歌词。我们使用从Spotify API检索的11个歌曲特征，组合歌词特征，包括情感，TF-IDF和Anew，以预测Deezer情绪检测数据集（DMDD）（Delbouys et al.，2018），具有4种不同的回归模型。我们发现，在11个高级歌曲特征中，主要有5个对性能有贡献，多模态特征在预测效价时比单独的音频做得更好。我们使我们的代码可供使用。
CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining
Audio Lab Hong Kong, Hong Kong Research Center, Huawei Technologies, Hong Kong SAR（香港音频实验室、香港研究中心、华为技术有限公司、香港特别行政区）；Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain（音乐技术组、庞培法华大学、巴塞罗那西班牙）；Munich Research Center, Huawei Technologies, Munich, Germany（慕尼黑研究中心、华为技术有限公司、慕尼黑德国）
Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval
National Institute of Informatics（国立信息学研究所）；The University of Electro-Communications（电气通信大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Hong Kong University of Science and Technology（香港科技大学）
MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing
Reid School of Music, University of Edinburgh（爱丁堡大学里德音乐学院）；Institute for Sport, Physical Education and Health Sciences (ISPEHS), University of Edinburgh（爱丁堡大学体育教育与健康科学研究所）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；Graduate School of Informatics, Kyoto University（京都大学情报学研究科）
