Williams Duncan, Hodge Victoria J, Wu Chia-Yu
Digital Creativity Labs, University of York, York, United Kingdom.
Department of Computer Science, University of York, York, United Kingdom.
Front Artif Intell. 2020 Nov 19;3:497864. doi: 10.3389/frai.2020.497864. eCollection 2020.
Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reduction of cases of dementia in elderly populations, and improvements in markers of general mental well-being such as stress reduction. Here, we describe short case studies addressing general mental well-being (anxiety, stress-reduction) through AI-driven music generation. Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range. However, access to music-making can be prohibitive in terms of access to expertize, materials, and cost. Furthermore the use of existing music for functional outcomes (such as targeted improvement in physical and mental health markers suggested above) can be hindered by issues of repetition and subsequent over-familiarity with existing material. In this paper, we describe machine learning approaches which create functional music informed by biophysiological measurement across two case studies, with target emotional states at opposing ends of a Cartesian affective space (a dimensional emotion space with points ranging from descriptors from relaxation, to fear). Galvanic skin response is used as a marker of psychological arousal and as an estimate of emotional state to be used as a control signal in the training of the machine learning algorithm. This algorithm creates a non-linear time series of musical features for sound synthesis "on-the-fly", using a perceptually informed musical feature similarity model. We find an interaction between familiarity and perceived emotional response. We also report on subsequent psychometric evaluation of the generated material, and consider how these - and similar techniques - might be useful for a range of functional music generation tasks, for example, in nonlinear sound-tracking such as that found in interactive media or video games.
越来越多的研究表明，音乐对身心健康都有益处，包括改善心血管健康、降低老年人群患痴呆症的几率，以及改善一般心理健康指标，如减轻压力。在此，我们描述了通过人工智能驱动的音乐生成来解决一般心理健康问题（焦虑、减轻压力）的简短案例研究。积极参与聆听和音乐创作活动（尤其是对高危年龄组）可能特别有益，并且音乐疗法已被证明在广泛年龄范围内的一系列用例中都有帮助。然而，在获取专业知识、材料和成本方面，进行音乐创作可能会受到限制。此外，使用现有音乐来实现功能结果（如上述针对性地改善身心健康指标）可能会因重复以及随后对现有材料过度熟悉的问题而受到阻碍。在本文中，我们描述了机器学习方法，该方法通过两个案例研究，根据生物生理测量生成功能性音乐，目标情绪状态位于笛卡尔情感空间（一个维度情感空间，其点的范围从放松到恐惧的描述符）的两端。皮肤电反应被用作心理唤醒的指标以及情绪状态的估计值，用作机器学习算法训练中的控制信号。该算法使用感知信息音乐特征相似性模型，实时创建用于声音合成的音乐特征非线性时间序列。我们发现熟悉度与感知到的情绪反应之间存在相互作用。我们还报告了对生成材料的后续心理测量评估，并考虑这些以及类似技术如何可能对一系列功能性音乐生成任务有用，例如在交互式媒体或视频游戏中发现的非线性声音跟踪。
