Musical Metacreation

从驾驶飞机到玩Jeopardy，人工智能在解决人类竞争水平的理性问题方面取得了巨大成功。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。创造性任务不同于典型的理性问题解决，因为最优性的概念定义不清。虽然搜索空间是明确定义的，但没有最佳的油画，肖像画，音乐创作，作品的解释，视频游戏的水平，叙事，诗歌，笑话等。在过去的5年里，我们的小组-MAMAS实验室-已经成功地开发了模型和系统，以产生旋律，和声进行，节奏模式，音景组成，以及成熟的电子舞曲作品，仅举几例。所有这些应用都依赖于一系列新的混合算法，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（以统计学上显著的方式）是人类的竞争力，也就是说，在类似的约束下产生的人类创造物是无法辨别的。拟议的研究项目建立在这些有希望的前提下，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（音高和起始，短语，部分，片段......）的音乐元创造算法。2.研究高层次的变量和跨模态映射策略，应使用，以引导互动环境中的音乐生成（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采用的科学方法将分别是：1.开发机器学习算法，以捕获音乐中的整体水平（时间）和垂直（谐波和音色）依赖性。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，原则上可以以无监督的方式学习几个抽象层次。2.探索各种方法来参数化这些模型并开发高级跨模态映射。例如，我们将在获奖的视频游戏关卡生成工作的基础上，使用游戏关卡的张力曲线以及玩家的情况来控制音乐的生成。3.为了使这些系统得到验证并随后被行业采用，人们需要证明它们具有人类竞争力。我们已经并将继续通过开发超越音乐图灵测试的定量实证实验的适当研究工具来满足这一具有挑战性的需求。随着非线性介质的发展，所需的资产数量呈指数级增长。除了它对人工智能、机器学习、计算创造力、计算机音乐和音乐信息检索的贡献之外，计算机辅助或完全程序化的音乐生成现在在各种软件产品（交互式系统、视频游戏等）中都是必需的。这是数字经济和加拿大工业在这些领域的核心。

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.