Engaging three user communities with applications and outcomes of computational music creativity

近年来，机器学习和人工智能系统在围棋和美国电视游戏节目《危险边缘》中击败人类而登上新闻头条！同样的技术也被应用到其他被认为本质上是人类的领域，比如艺术和音乐。该项目提出了人与机器范式的另一种观点：这些技术可以产生有用、有意义和无威胁的共同创作伙伴。该项目促进了四项创新和创造性活动，以参与三个用户社区：作曲家、表演者和听众。这个项目的主要目的是让这些用户社区参与到计算音乐创意的应用和成果中来。一个辅助目标是让研究人员从这些社区中协作地学习新兴计算音乐创造力在非学术背景下的含义。该项目的目标之一是开发一个软件应用程序，使任何连接到互联网的人都可以探索音乐的共同创作。该项目还将为该应用程序和其他应用程序的用户社区开发一个在线资源，用户可以在其中分享、评论和评估提交的结果。支持这一应用的技术来自AHRC资助的未来关怀研究项目，“通过历史研究卡利普索节奏的数据科学”(DaCaRyH，AH/N504531/1)。该项目汇集了民族音乐学家、数据科学专家和一名作曲家，研究如何使用计算工具来研究卡利普索音乐，以及如何利用对音乐的研究来为计算分析的设计提供信息。DaCaRyH的一个意想不到的结果是，这些分析工具中的一些可以被“颠倒”来创建系统，合成以前从未听过的音乐，这是令人惊讶的好。调查人员的初步工作(以专家启发、公共工作坊和音乐会的形式)表明，听众和实践者对探索学术领域以外的计算创造力有着极大的兴趣。该项目将使公众观众参与到由计算共同创造力产生的音乐中。它将为作曲家提供一个动手研讨会，让他们学习使用开发的软件应用程序。此外，该会亦会为市民举办一个有关电脑音乐创意的研讨会。该项目的结束将以一场音乐会为特色，展示用开发的应用程序创建的新音乐。为了鼓励参与，该项目将组织一场比赛，征集与该应用程序一起创作的原创作品，由专业乐团表演。比赛评审团将包括音乐家、作曲家，当然还有一位使用人工智能构建的评论家。这个项目的一个特别新奇的成果是一张专业制作的“机器民谣”音乐专辑。这张专辑将以完全由系统生成的音乐为特色，并由经验丰富的音乐家编辑、编曲和表演。研究人员的初步工作发现，优秀音乐家演奏的好音乐是计算创造力的价值和潜在用处的最佳大使。这张专辑将作为机器学习工具联合创作方法的好处的持久例证。该项目通过将最先进的数字技术引入到创造性音乐制作的民主化参与中，直接与数字转型主题联系在一起。它还与关心未来主题有关，鼓励社区音乐实践中的创新和更新，从而有助于扩大和深化听众、表演者和作曲家的参与。该项目将有助于使DaCaRyH项目的遗产多样化。

In recent years, machine learning and artificial intelligence systems have been grabbing headlines by defeating humans at the game of Go and the American TV game-show Jeopardy! The same technology have also been applied to other domains considered essentially human, like art and music. This project proposes an alternative viewpoint to the human-vs-machine paradigm: these technologies can produce co-creative partners that are useful, meaningful, and non-threatening.This project facilitates four innovative and creative activities to engage with three user communities: Composers, Performers, and Listeners. The principal aim of this project is to engage these user communities with applications and outcomes of computational music creativity. A subsidiary aim is for the investigators to collaboratively learn from these communities about the implications of emergent computational music creativity in non-academic contexts. Among its objectives, this project will develop a software application to enable anyone with an Internet connection to explore music co-creation. The project will also develop an online resource for the user community of this application and others, where users can share, comment and rate submitted outcomes. The technology underpinning this application comes from the AHRC-funded Care for the Future research project, "Data science for the study of calypso-rhythm through history" (DaCaRyH, AH/N504531/1). That project brings together ethnomusicologists, experts in data science and a composer to investigate how computational tools can be used to study calypso music, and how the study of music can be used to inform the design of computational analyses. An unanticipated outcome of DaCaRyH is that some of these analytical tools can be "inverted" to create systems that synthesise never-before-heard music that is surprisingly good. Preliminary work by the investigators (in the form of expert elicitations, a public workshop, and music concert) show there is great interest among listeners and practitioners for exploring computational creativity outside of the academic realm.The project will engage public audiences with music arising from computational co-creativity. It will deliver a hands-on workshop for composers to learn to use the developed software application. It will also organise a symposium on computational music creativity for the general public. The conclusion of the project will feature a music concert showcasing new music created with the developed application. To motivate participation, the project will organise a competition to solicit original compositions created with the application, to be performed by a professional ensemble. The competition jury will include musicians, composers, and of course a critic built using artificial intelligence. An especially novel outcome of this project is a professionally produced album of "machine folk" music. The album will feature music generated entirely by the system and edited, arranged, and performed by experienced musicians. The preliminary work of the investigators has found that good music performed by good musicians serves as the best ambassador for the value and potential usefulness of computational creativity. This album will serve as a lasting illustration for the benefits of the co-creative approach to machine learning tools.This project links directly with the Digital Transformations theme by bringing state of the art digital technology to democratise participation in creative music making. It also relates to the Care for the Future theme by encouraging innovation and renewal in community music practice, and thus helping to broaden and deepen engagement from listeners, performers and composers. The project will serve to diversify the legacy of the DaCaRyH project.