Transformer is a successful deep neural network (DNN) architecture that has shown its versatility not only in natural language processing but also in music information retrieval (MIR). In this paper, we present a novel Transformer-based approach to tackle beat and downbeat tracking. This approach employs SpecTNT (Spectral-Temporal Transformer in Transformer), a variant of Transformer that models both spectral and temporal dimensions of a time-frequency input of music audio. A SpecTNT model uses a stack of blocks, where each consists of two levels of Transformer encoders. The lower-level (or spectral) encoder handles the spectral features and enables the model to pay attention to harmonic components of each frame. Since downbeats indicate bar boundaries and are often accompanied by harmonic changes, this step may help downbeat modeling. The upper-level (or temporal) encoder aggregates useful local spectral information to pay attention to beat/downbeat positions. We also propose an architecture that combines SpecTNT with a state-of-the-art model, Temporal Convolutional Networks (TCN), to further improve the performance. Extensive experiments demonstrate that our approach can significantly outperform TCN in downbeat tracking while maintaining comparable result in beat tracking.
HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking
School of Computer Science, Fudan University, Shanghai, China（复旦大学计算机科学学院）；Naval Medical Center, PLA, China（中国人民解放军海军医疗中心）；Graduate School of Informatics, Kyoto University, Kyoto, Japan（京都大学信息科学研究生院）；Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan（广岛大学先进科学与工程研究生院）；Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China（上海智能信息处理重点实验室）
BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model
School of Computer Science, Fudan University, Shanghai, China（复旦大学计算机科学学院）；Naval Medical Center, PLA, China（中国人民解放军海军医疗中心）；Graduate School of Informatics, Kyoto University, Kyoto, Japan（京都大学信息科学研究生院）；Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan（广岛大学先进科学与工程研究生院）；Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China（上海智能信息处理重点实验室）
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）
Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model
Ulsan National Institute of Science and Technology, South Korea（乌山国立科学技术研究院，韩国）；Daegu Gyeongbuk Institute of Science and Technology, South Korea（大邱庆北科学技术研究院，韩国）
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
The Hong Kong University of Science and Technology（香港科技大学）；Nanjing University of Science and Technology（南京理工大学）；Fudan University（复旦大学）；Microsoft Research Asia（微软亚洲研究院）；Tsinghua University（清华大学）
Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
Fraunhofer Institute for Software and Systems Engineering ISST（弗劳恩霍夫软件与系统工程研究所）；Department of Computer Science, University of Applied Sciences and Arts Dortmund（应用科学大学多特蒙德计算机科学系）；Department of Computer Science, TU Dortmund University（图尔努恩大学计算机科学系）；Department of Health Informatics, School of Medicine, Witten/Herdecke University（医学学院健康信息学系，维滕/赫尔德克大学）；Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen（医学信息学、生物统计与流行病学研究所（IMIBE），埃森大学医院）
