AI in Computational Arts, Music, and Games

The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology, matters of fact and concern and feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine the implicit, the wider connections and entangled filaments in Human–AI musical interactions.

The acoustic modeling of musical instruments is a heavy computational process, often bound to the solution of complex systems of partial differential equations (PDEs). Numerical models can achieve a high level of accuracy, but they may take up to several hours to complete a full simulation, especially in the case of intricate musical mechanisms. The application of deep learning, and in particular of neural operators that learn mappings between function spaces, has the potential to revolutionize how acoustics PDEs are solved and noticeably speed up musical simulations. However, extensive research is necessary to understand the applicability of such operators in musical acoustics; this requires large datasets, capable of exemplifying the relationship between input parameters (excitation) and output solutions (acoustic wave propagation) per each target musical instrument/configuration. With this work, we present an open-access, open-source framework designed for the generation of numerical musical acoustics datasets and for the training/benchmarking of acoustics neural operators. We first describe the overall structure of the framework and the proposed data generation workflow. Then, we detail the first numerical models that were ported to the framework. This work is a first step towards the gathering of a research community that focuses on deep learning applied to musical acoustics, and shares workflows and benchmarking tools.

How do we challenge the norms of AI voice technologies? What would be a non-normative approach in finding novel artistic possibilities of speech synthesis and text-to-speech with Deep Learning? This paper delves into SpeechBrain, OpenAI and CoquiTTS voice and speech models with the perspective of an experimental vocal practitioner. Exploratory Research-through-Design guided an engagement with pre-trained speech synthesis models to reveal their musical affordances in an experimental vocal practice. We recorded this engagement with voice and speech Deep Learning technologies using auto-ethnography, a novel and recent methodology in Human-Computer Interaction. Our position in this paper actively subverts the normative function of these models, provoking nonsensical AI-mediation of human vocality. Emerging from a sense-making process of poetic AI nonsense, we uncover the generative potential of non-normative usage of normative speech recognition and synthesis models. We contribute with insights about the affordances of Research-through-Design to inform artistic working processes with AI models; how AI-mediations reform understandings of human vocality; and artistic perspectives and practice as knowledge-creation mechanisms for working with technology.

Technological advancements in deep learning for speech and voice have contributed to a recent expansion in applications for voice cloning, synthesis and generation. Invisibilised stakeholders in this expansion are numerous absent bodies, whose voices and voice data have been integral to the development and refinement of these speech technologies. This position paper probes current working practices for voice and speech in machine learning and AI, in which the bodies of voices are “invisibilised". We examine the facts and concerns about the voice-Body in applications of AI-voice technology. We do this through probing the wider connections between voice data and Schaefferian listening; speculating on the consequences of missing Bodies in AI-Voice; and by examining how vocalists and artists working with synthetic Bodies and AI-voices are ‘bringing the Body back’ in their own practices. We contribute with a series of considerations for how practitioners and researchers may help to ‘bring the Body back’ into AI-voice technologies.

The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?