Apolo: a toolkit for generative music production and benchmarking

Apolo：用于生成音乐制作和基准测试的工具包

• 批准号: RGPIN-2019-07093
• 负责人: Pasquier, Philippe
• 金额: $ 4.01万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739305
• 关键词: Apolo; toolkit; generative; music; production

The Apolo project is a direct response to recent developments in Artificial Intelligence, and Machine Learning when applied to the partial or complete automation of creative tasks, a field known as computational creativity, Creative AI, or Metacreation. In particular, recent advances in corpus-based generative music algorithms are leading to a revolution in how music is being created by professionals and amateurs alike. These increasingly powerful algorithms have not yet been applied much, mostly due to the complexities of machine learning, and the lack of an open source library, data management software, and graphical user interfaces for accessing these algorithms. The Apolo project aims to fill this gap providing user-friendly technologies that enable composers, sound designers, game developers, VR studios, amateur musicians as well as researchers and students to understand, apply, refine, benchmark, extend these new computational techniques in their own applications or research. The main outcomes of the Apolo project will be: - Generative algorithms that go beyond pastiche and style imitation using novelty and surprise search in combination with Deep Learning generative algorithms - High-level variables and cross-modal mapping strategies that can be used to steer the musical generation in interactive contexts be it for computer-assisted composition in digital audio workstations, or adaptive online generation in online applications (such as video games). - The ApoloC corpus manager will allow users (researchers, students, musicians, composer, and enthusiasts) to load and manipulate music compositions (in a variety of formats) to manage training sets for generative style imitation algorithms. - The ApoloG generation engine, will provide a graphic interface to select training sets, training algorithms, and specify the generative task and parameters. - Both ApoloC and ApoloG will rest on the Apolo library: an open source library implementing a collection of existing and new music generation algorithms. - The ApoloQ quantitative evaluation toolbox for style imitation will be integrated into the framework to help benchmark algorithms. With the shift from linear to non-linear media, users are now exposed to rich immersive and interactive experiences. Be it for gaming, education, or entertainment, interactive mediums are quickly outgrowing linear media (books, film, tv, ...). As nonlinear (interactive) mediums develop, the number of assets needed is growing exponentially. Besides its contribution to artificial intelligence, machine learning, computational creativity, computer music, and music information retrieval, the Apolo project will lay the ground for computer-assisted or completely procedural music generation approaches that are now required in a variety of software products (interactive systems, video games, ...), central to the digital economy and Canadian industry in these sectors.

Apolo项目是对人工智能的最新发展的直接响应，当将机器学习应用于创意任务的部分或完整自动化时，该领域被称为计算创造力，创造性AI或MetaCration。特别是，基于语料库的生成音乐算法的最新进展正在导致专业人士和业余爱好者如何创造音乐的革命。这些越来越强大的算法尚未应用太多，主要是由于机器学习的复杂性，缺乏开源库，数据管理软件和用于访问这些算法的图形用户界面。 Apolo项目旨在填补这一空白，从而为用户友好的技术提供，使作曲家，声音设计师，游戏开发人员，VR Studios，业余音乐家以及研究人员和学生能够在其应用程序或研究中扩展这些新的计算技术。 Apolo项目的主要结果将是： - 使用新颖性和惊喜搜索超出了生成算法，并结合深度学习生成算法 - 高级变量 - 高级变量和可以用于在线互动中的互动上环境中的互动上环境（以在线互动的互动）为数字化的工具（或在数字化的互动中）（或在数字上）进行数字化工作（或在数字化的互动中）。 - Apoloc语料库经理将允许用户（研究人员，学生，音乐家，作曲家和爱好者）加载和操纵音乐作品（以各种格式）来管理用于生成风格的模仿算法的培训集。 - 律师生成引擎将提供一个图形接口，以选择训练集，训练算法并指定生成任务和参数。 - Apoloc和Dage都将放在Apolo库：一个开源库，该库实施了现有音乐生成算法的集合。 - 模仿样式模仿的Apoloq定量评估工具箱将集成到框架中，以帮助基准算法。随着从线性到非线性媒体的转变，用户现在接触到丰富的沉浸式和互动体验。无论是为了游戏，教育还是娱乐，交互式媒介都会快速超越线性媒体（书籍，电影，电视，...）。随着非线性（交互式）培养基的发展，所需的资产数量正在成倍增长。除了其对人工智能，机器学习，计算创造力，计算机音乐和音乐信息检索的贡献外，Apolo项目还将为现在在这些行业中数字经济和加拿大行业的核心的软件产品（交互式系统，视频游戏，...）中所需的计算机辅助或完全程序上的音乐生成方法奠定基础。