Musical Metacreation

从驾驶飞机到玩危险游戏，人工智能在人类竞争层面的理性问题解决方面取得了巨大的成功。计算创造力是一个快速发展的领域，致力于将创造性任务自动化，即赋予机器创造性行为，作为理性问题解决的补充。创造性任务与典型的理性解决问题的不同之处在于，最优性的概念定义不明确。虽然搜索空间定义得很好，但没有像油画、肖像素描、音乐作曲、作曲解释、视频游戏水平、叙事、诗歌、笑话等这样的东西。音乐元创造是应用于计算机音乐的计算创造力的子领域。**在过去的5年里，我们的团队-Mamas实验室-已经成功地开发了模型和系统来生成旋律、和声序列、节奏模式、音景作品和成熟的电子舞蹈音乐作品，仅举几例。所有这些应用都依赖于一组新的和混合的算法，这些算法集成了人类作曲家的专业知识、计算机音乐和音乐信息检索技术、进化计算和基于语料库的机器学习。这些系统中的大多数已经被证明(以统计上显著的方式)是人类竞争的，这与在类似约束下产生的人类创作是分不开的。**提议的研究项目建立在这些有希望的前提上，具有以下目标，这些目标是对该领域当前挑战的直接回答：*1.开发能够学习多层次音乐结构(音高和开始、短语、部分、片段)的音乐元创作算法。*2.研究应该使用的高级变量和跨模式映射策略，以便在交互环境中引导音乐生成(数字音频工作站中的计算机辅助创造力，视频游戏中的自适应在线生成。*3.计算创造力的进一步评估方法。**为实现这三个目标而部署的科学方法将分别是：*1.开发机器学习算法，以捕获音乐中总体的水平(时间)和垂直(调和和音调)依赖关系。为此，我们将适应、应用和比较各种机器学习范式，例如：进化计算(GP，笛卡尔规划)和分层、多级隐马尔可夫模型、递归神经网络和深度学习算法，这些算法原则上可以以无监督的方式学习几个抽象级别。*2.探索这些模型的各种参数化方法和开发高级跨模式映射。例如，我们将在我们之前关于视频游戏级别生成的获奖工作的基础上，使用游戏级别的张力曲线以及玩家的情况来控制音乐生成。*3.为了使这些系统得到验证并随后被行业采用，人们需要证明它们是具有人类竞争力的。我们已经并将继续为这一具有挑战性的需求做出贡献，开发基于定量实证实验的适当研究工具，这些实验超越了音乐图灵测试。**随着非线性介质的发展，所需资产的数量呈指数级增长。除了对人工智能、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，现在各种软件产品(交互系统、视频游戏等)都需要计算机辅助或完全过程化的音乐生成。对数字经济和加拿大这些行业的工业来说是核心的。

From flying planes to playing Jeopardy, Artificial Intelligence has been tremendously successful at rational problem solving at a human competitive level. Computational creativity is the fast growing field devoted to automatizing creative tasks, that is endow machine with creative behavior, as a complement to rational problem solving. Creative tasks differ from typical rational problem solving in the sense that the notion of optimality is ill defined. While the search spaces are well defined, there is no such thing as an optimal oil painting, portrait drawing, music composition, interpretation of a composition, level for a video game, narrative, poetry, joke, etc. Musical Metacreation is the sub-field of computational creativity applied to computer music.**In the last 5 years, our group - the MAMAS laboratory - has been successful at developing models and systems to generate melodies, harmonic progression, rhythmic patterns, soundscape compositions, and full-fledged electronic dance music pieces, to name a few. All of these applications rest on a collection of new and hybrid algorithms that integrate human composer expertise, computer music and music information retrieval techniques, evolutionary computation, and corpus-based machines learning. Most of these systems have been shown (in statistically significant ways) to be human competitive, that is to be indiscernible from human creations produced under similar constraints.**The proposed research project builds on these promising premises, with the following objectives which are direct answers to the current challenges in the field:*1. Develop algorithms for Musical Metacreation that are capable of learning multiple levels of musical structure (pitch and onsets, phrases, parts, pieces, .).*2. Research the high level variables and cross-modal mapping strategies that shall be used in order to steer the musical generation in interactive contexts (computer-assisted creativity in digital audio workstations, adaptive online generation in Video games, .).*3. Further evaluation methodologies for computational creativity.**The scientific approaches deployed to tackle these three objectives will respectively be:*1. Developing machine learning algorithms to capture the overall horizontal (temporal) and vertical (harmonic and timbral) dependencies in music. To this effect, we will adapt, apply and compare various machine learning paradigms such as: evolutionary computation (GP, Cartesian Programming) and hierarchical, multi-level hidden Markov Models, recurrent neural networks, and Deep Learning algorithms which can - in principle - learn several levels of abstraction in an unsupervised fashion.*2. Exploring various ways to parametrize these models and develop high-level cross-modal mappings. For example, we will build on our award-winning previous work on video game level generation and use the tension curve of the game level as well as the situation of the player to control the music generation.*3. In order for these systems to be validated and later adopted by the industry, one need to show that they are human competitive. We have, and will continue to contribute to this challenging need by developing appropriate research instruments resting on quantitative empirical experiments that go beyond the musical Turing test.**As non-linear medium develops, the number of assets needed is growing exponentially. Beside its contribution to artificial intelligence, machine learning, computational creativity, computer music, and music information retrieval, computer assisted or completely procedural music generation is now required in a variety of software products (interactive systems, video games, ...) that are central to the digital economy and Canadian industry in these sectors.