Creative Chord Sequence Generation for Electronic Dance Music

被引:3
|
作者
Conklin, Darrell [1 ,2 ]
Gasser, Martin [3 ]
Oertl, Stefan [3 ]
机构
[1] Univ Basque Country UPV EHU, Dept Comp Sci & Artificial Intelligence, San Sebastian 20018, Spain
[2] Basque Fdn Sci, Ikerbasque, Bilbao 48013, Spain
[3] Re Compose GmbH, A-1080 Vienna, Austria
来源
APPLIED SCIENCES-BASEL | 2018年 / 8卷 / 09期
基金
欧盟地平线“2020”;
关键词
music generation; chord sequences; harmony; MODELS;
D O I
10.3390/app8091704
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This paper describes the theory and implementation of a digital audio workstation plug-in for chord sequence generation. The plug-in is intended to encourage and inspire a composer of electronic dance music to explore loops through chord sequence pattern definition, position locking and generation into unlocked positions. A basic cyclic first-order statistical model is extended with latent diatonicity variables which permits sequences to depart from a specified key. Degrees of diatonicity of generated sequences can be explored and parameters for voicing the sequences can be manipulated. Feedback on the concepts, interface, and usability was given by a small focus group of musicians and music producers.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] The Subversive Sociocultural Meanings of Cantopop Electronic Dance Music
    Chew, Matthew M.
    [J]. CHINESE SOCIOLOGY AND ANTHROPOLOGY, 2009, 42 (02): : 76 - 93
  • [42] Hybridity, Empowerment and Subversiveness in Cantopop Electronic Dance Music
    Chew, Matthew
    [J]. VISUAL ANTHROPOLOGY, 2010, 24 (1-2) : 139 - 151
  • [43] Pleasurable and Intersubjectively Embodied Experiences of Electronic Dance Music
    Solberg, Ragnhild Torvanger
    Jensenius, Alexander Refsum
    [J]. EMPIRICAL MUSICOLOGY REVIEW, 2016, 11 (3-4): : 301 - 318
  • [44] The Functions of Continuous Processes in Contemporary Electronic Dance Music
    Smith, Jeremy W.
    [J]. MUSIC THEORY ONLINE, 2021, 27 (02):
  • [45] TG-Dance: TransGAN-Based Intelligent Dance Generation with Music
    Huang, Dongjin
    Zhang, Yue
    Li, Zhenyan
    Liu, Jinhua
    [J]. MULTIMEDIA MODELING, MMM 2023, PT I, 2023, 13833 : 243 - 254
  • [46] Soul Train: The Music, Dance, and Style of a Generation.
    Salois, Kendra
    [J]. JOURNAL OF POPULAR MUSIC STUDIES, 2014, 26 (2-3) : 414 - 418
  • [47] Robot Dance Generation with Music Based Trajectory Optimization
    Boukheddimi, Melya
    Harnack, Daniel
    Kumar, Shivesh
    Kumar, Rohit
    Vyas, Shubham
    Arriaga, Octavio
    Kirchner, Frank
    [J]. 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, : 3069 - 3076
  • [48] Perceptually motivated automatic dance motion generation for music
    Kim, Jae Woo
    Fouad, Hesham
    Sibert, John L.
    Hahn, James K.
    [J]. COMPUTER ANIMATION AND VIRTUAL WORLDS, 2009, 20 (2-3) : 375 - 384
  • [49] Dance2Music-Diffusion: leveraging latent diffusion models for music generation from dance videos
    Zhang, Chaoyang
    Hua, Yan
    [J]. Eurasip Journal on Audio, Speech, and Music Processing, 2024, 2024 (01)
  • [50] From dance floor to big screen Electronic dance music in the cinema of the end of the millennium
    Corbella, Maurizio
    [J]. PHILOMUSICA, 2014, 13 (02): : 111 - 134