Since the introduction of deep learning, researchers have proposed content generation systems using deep learning and proved that they are competent to generate convincing content and artistic output, including music. However, one can argue that these deep learning-based systems imitate and reproduce the patterns inherent within what humans have created, instead of generating something new and creative. This paper focuses on music generation, especially rhythm patterns of electronic dance music, and discusses if we can use deep learning to generate novel rhythms, interesting patterns not found in the training dataset. We extend the framework of Generative Adversarial Networks(GAN) and encourage it to diverge from the dataset's inherent distributions by adding additional classifiers to the framework. The paper shows that our proposed GAN can generate rhythm patterns that sound like music rhythms but do not belong to any genres in the training dataset. The source code, generated rhythm patterns, and a supplementary plugin software for a popular Digital Audio Workstation software are available on our website.
National Institute of Science and Technology for Complex Systems（国家复杂系统科学技术研究所）
Online music databases have increased signicantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is build in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multivariate st…
Dance-to-Music Generation with Encoder-based Textual Inversion
Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；University of Konstanz（康斯坦茨大学）；National Cheng-Kung University（成功大学）
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Space-Ground Integrated Information Technology（空间地面一体化信息技术创新实验室）
Benchmarking Sub-Genre Classification For Mainstage Dance Music
Johns Hopkins University（约翰霍普金斯大学）；Southeast University（东南大学）；National University of Defense Technology（国防科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Music Genre Classification: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches
Computer Science and Engineering（计算机科学与工程）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tribhuvan University, IOE（特里布文大学理工学院）；Arlington, USA（美国阿灵顿）；kathmandu（加德满都）；Dharan（达尔汉）
GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
School of Computer Science and Electronic Engineering, University of Surrey（萨里大学计算机科学与电子工程学院）；Department of Music and Media, University of Surrey（萨里大学音乐与媒体系）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）
Queen Mary University of London（伦敦玛丽女王大学）；Indian Institute of Technology Kharagpur（印度理工学院哈拉格布尔分校）；National Taiwan University（国立台湾大学）；Dadabots（达达机器人）
Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include information on expressive techniques and fingerings for fretted string instruments in addition to rhythm and pitch. In this work, we use the DadaGP dataset for guitar tab music generation, a corpus of over 26k songs in GuitarPro and token formats. We introduce methods to condition a Tra…
ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement
Electronic Engineering and Computer Science Department, Queen Mary University of London, UK（伦敦大学女王学院电子工程与计算机科学系）；Information Systems, Technology, and Design, Singapore University of Technology and Design, Singapore（新加坡科技设计大学信息系统、科技与设计系）
Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Hohai university（河海大学）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Institute for Infocomm Research (I 2 R), A*STAR（新加坡科技研究局信息与通信研究所）
Nankai University（南开大学）；National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Tsinghua University（清华大学）
Definitive embeddings remain a fundamental challenge of computational musicology for symbolic music in deep learning today. Analogous to natural language, music can be modeled as a sequence of tokens. This motivates the majority of existing solutions to explore the utilization of word embedding models to build music embeddings. However, music differs from natural languages in two key aspects: (1) musical token is multi-faceted -- it comprises of pitch, rhythm and dynamics information; and (2) musical context is two-dimensional -- each musical token is dependent on both melodic and harmonic con…
Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional information. In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information. Specifically, we study the effects of four different conditioning inputs on the performance of a recurrent monophonic melody generation model. …
Beihang University（北京航空航天大学）；Charterhouse School（查特豪斯公学）；Sea AI Lab
In this work, we address the task of video background music generation. Some previous works achieve effective music generation but are unable to generate melodious music tailored to a particular video, and none of them considers the video-music rhythmic consistency. To generate the background music that matches the given video, we first establish the rhythmic relations between video and background music. In particular, we connect timing, motion speed, and motion saliency from video with beat, simu-note density, and simu-note strength from music, respectively. We then propose CMT, a Controllabl…
Synthesize human motions from music, i.e., music to dance, is appealing and attracts lots of research interests in recent years. It is challenging due to not only the requirement of realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm and melody of the music. In this paper, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals to generate 3D dance motions with high realism and diversity. To boost the performance of our proposed model, …
This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective. This thesis focuses on the information provided by the visual layer of music videos and how it can be harnessed to augment and improve tasks of the MIR research domain. The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone, without the sound being heard. This leads to the hypothesis that there exists a visual language that is used to…
Bremen Spatial Cognition Center, University of Bremen（不来梅大学不来梅空间认知中心）；Faculty of Information and Communication Technology, Mahidol University（玛希隆大学信息与通信技术学院）
In the recording studio, producers of Electronic Dance Music (EDM) spend more time creating, shaping, mixing and mastering sounds, than with compositional aspects or arrangement. They tune the sound by close listening and by leveraging audio metering and audio analysis tools, until they successfully creat the desired sound aesthetics. DJs of EDM tend to play sets of songs that meet their sound ideal. We therefore suggest using audio metering and monitoring tools from the recording studio to analyze EDM, instead of relying on conventional low-level audio features. We test our novel set of featu…
Black Sesame Technologies（黑芝麻科技）；Nanyang Technological University（南洋理工大学）；University of Alberta（阿尔伯塔大学）
Dance choreography for a piece of music is a challenging task, having to be creative in presenting distinctive stylistic dance elements while taking into account the musical theme and rhythm. It has been tackled by different approaches such as similarity retrieval, sequence-to-sequence modeling and generative adversarial networks, but their generated dance sequences are often short of motion realism, diversity and music consistency. In this paper, we propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographies from music. We introduce an o…
Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）
Bharatanatyam, an Indian Classical Dance form, represents the rich cultural heritage of India. Analysis and recognition of such dance forms are critical for the preservation of cultural heritage. Like in most dance forms, a Bharatanatyam dancer performs in synchronization with structured rhythmic music, called Sollukattu, which comprises instrumental beats and vocalized utterances (bols) to create a rhythmic music structure. Computer analysis of Bharatanatyam, therefore, requires a structural analysis of Sollukattus. In this paper, we use speech processing techniques to recognize bols. Exploit…
National Technical University of Athens（雅典国家技术大学）；National and Kapodistrian University Of Athens（雅典国立卡波迪斯特里亚大学）；National Center for Scientific Research - Demokritos（国家科学研究中心——德谟克利特）；Athena RC（雅典娜研究中心）
Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that i…
Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on the more challenging EDM subgenre classification. The state-of-art model is based on extremely randomized trees and could be improved by deep learning methods. In this paper, we extend the state-of-art music auto-tagging model "short-chunkCNN+Resnet" to EDM subgenre classification…
Eindhoven University of Technology（埃因霍温理工大学）；University of Florence（佛罗伦萨大学）；University of Basel（巴塞尔大学）；Conservatorio G.F. Ghedini di Cuneo（库尼奥G.F.盖迪尼音乐学院）；Musica Combinatoria（组合音乐机构）
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain. Experimental results reveal that generated patterns can be employed to produce musically sound and creative transitions between different genres, and that the process of generation is of interest to practitioners in the field.
Exploring Multi-Modal Control in Music-Driven Dance Generation
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
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）
Universitat Pompeu Fabra（庞培法布拉大学）；Universidad de Sevilla（塞维利亚大学）
Flamenco is a music tradition from Southern Spain which attracts a growing community of enthusiasts around the world. Its unique melodic and rhythmic elements, the typically spontaneous and improvised interpretation and its diversity regarding styles make this still largely undocumented art form a particularly interesting material for musicological studies. In prior works it has already been demonstrated that research on computational analysis of flamenco music, despite it being a relatively new field, can provide powerful tools for the discovery and diffusion of this genre. In this paper we p…
KTH Royal Institute of Technology（皇家理工学院）；Nanyang Technological University（南洋理工大学）；National Institute of Informatics（情报学研究所）；University of Copenhagen（哥本哈根大学）
Content-based Controls For Music Large Language Modeling
New York University Shanghai（上海纽约大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Queen Mary University of London（伦敦玛丽女王大学）
