ABSTRACT. The intersection of popular music composition and artificial intelligence (AI) is a dynamic and rapidly evolving field. With the rise of AI music generation and compositional tools, this technology has the potential to revolutionise the music creation process. Between assisting composers with writer's block, to providing feedback and suggestions on provided samples or recorded tracks, AI provides innovative and exciting techniques for creatives to explore. Despite its potential, there remains significant resistance regarding the use of this technology. This paper explores past and present AI music tools, specifically text-to-audio technology and tools which assist within the songwriting process. Additionally, this paper introduces a wider PhD study which aims to demonstrate how AI can support the creative process for artists and explores what that collaborative experience may look like.

ABSTRACT. The Whisperers in the Darkness by H. P. Lovecraft is a tale of otherworldly horror where a scholar discovers evidence of extraterrestrial beings. Capable of vast technological and psychic powers, these beings are also able to “give life” to a human voice. The author crafts a narrative that transcends horror to probe the unsettling edges of science, and the novel can be seen as an early, prophetic exploration of today’s concerns about voice cloning technology. Lovecraft’s whisperer, an enigmatic entity communicating across unfathomable distances, eerily prefigures today's advancements in artificial intelligence (AI) and ultra-realistic vocal synthesis. With its focus on the inexplicable and yet perceivable, and its obsession with the manipulation of human perception, the novel’s narrative serves as a metaphor for the problematic nature of vocal clones. Equally, it embodies both the allure and the vast potential for applications, along with a sense of awe and a profound reevaluation of one’s place in the “system.” Just as the novel’s character grapples with forces (and vocalities) beyond his understanding, we too stand on the precipice of an ontological abyss. Voice cloning, with its potential for deception, with the legal and ethical risks it introduces, and with its challenge to the authenticity of human interaction, echoes Lovecraft’s themes of unseen influence and the fragility of reality. In his portrayal of the otherly, Lovecraft anticipates contemporary anxieties surrounding truth and identity, recalling Mark Fisher's thinking on the “weird and the eerie,” and the disruption of the familiar. Meanwhile, as technologies progress towards increasingly realistic models, vocal clones daily descend further into Mori’s uncanny valley. This paper invites us to question the implications of increasingly realistic clones, reminding us that this pursuit may leave us unable to discern our own voices from those we have conjured. An overview of the existing technology and services available is provided, in an exploration of their potential applications in pedagogy. Echoing increasing concerns around privacy, consent, and fraud, the paper advocates for a vigilant approach. Nonetheless, it discusses voice clones as potential allies in learning. Lovecraft’s Whisperers is thus presented as a cautionary reflection on the advantages and challenges of AI. The aim is to initiate an interdisciplinary dialogue on the transformative power of technology in education, emphasizing the responsibility of didactics in the future of human communication.

ABSTRACT. Computers may now compose new music on their own thanks to artificial intelligence. How this AI-generated music affects essential human skills such as creativity and cognition is mostly unknown. The study describes an inquiry into whether exposure to AI-created music improves divergent thinking, problem-solving abilities, focus, memory consolidation and cognitive flexibility. Both qualitative and quantitative approaches, such as experiments, neuroimaging studies and comparative analyses, will be used to investigate how engaging with these machines-composed soundscapes influences creative output and cognitive processes in comparison to traditional musical compositions. Using data-driven AI models to generate music with certain qualities (Huston, 2022), stimuli will be created to test established hypotheses about memorability, unexpected novelty, and personalised aspects that optimise attention. The present study responsibly advances understanding of the intersection of artificial and human creativity by incorporating ethical considerations such as consent, bias mitigation, and avoiding overstated claims, while also assessing the potential for AI systems to eventually design tailored musical interventions boosting cognition across educational, occupational, and healthcare contexts. Pioneering research in this area demonstrated the potential of these technologies, although with certain limits (Huang et al., 2022). The research thus thoroughly examines the growing intersection between AI Composition, music perception, and the creative mind.

Aim

The primary goal of this study is to advance the knowledge of how algorithmically composed music generated by artificial intelligence systems impacts basic aspects of human psychology and behaviour. Specifically, it aims to give information on the developing interaction between AI-music-generating technologies and complicated higher-order cognitive skills such as creativity, memory, attention, and problem-solving.

On the dimension of creativity, the research has a two-pronged orientation: first, to determine if exposure to innovative soundscapes created by AI composing algorithms might improve attributes associated with creative cognition in humans, such as divergent thinking, adaptability, and original ideas. Second, it intends to examine the creative process from the other side, exploring how partnering with these algorithms affects music composers, including measures such as idea development, overcoming creative blockages, and satisfaction with the final music composition. In terms of cognitive functions, the goal is to see if the AI system’s built-in customizability and generative nature can be used to optimise features such as memorability, concentration, attention modulation, and structural novelty in algorithmically generated music to precisely target and boost specific cognitive capacities when humans interact with it.

Methodology The research employs qualitative methodologies to collect subjective opinions on the experience of engaging with AI-generated music, as well as conceptual foundation via a survey of relevant literature. In-depth interviews with artists, composers, and producers have documented their firsthand reactions to working with algorithmic music systems. Impacts on musical inspiration, overcoming creative barriers, co-creative processes, and perceived benefits or drawbacks are investigated. Surveys also gathered feedback from a larger range of listeners on the emotional, visceral, and meaningful components of AI music.

Furthermore, the study combines findings from the literature on the effects of music listening on creativity, cognition, and well-being. Connections across fields such as music psychology, neurology, and human-computer interaction research lead to ideas and theoretical frameworks for the mechanisms behind AI music's possible impacts.

Outcome(s) First-person interviews with artists and composers revealed a wide range of creative experiences, from those who feel newly inspired and energised by productive "sparring" against their AI collaborator to those who are frustrated by technological limitations or perceive the music as soulless. The present research also reveals the substance of these reactions, identifying common inspiration triggers and problems impeding creative flow at the human-AI interface.

Surveys of listener groups showcase demographic tendencies in preferences for human vs machine composition, as well as effects on emotional aspects like depth and originality. Text analysis also reveals relationships between word choice and satisfaction. A review of the scattered literature helped to crystallise key hypotheses about music cognition into an integrated conceptual framework for determining whether and how structural novelty and customised variability hypothesised in AI systems may distinctly activate and enhance neural processes related to creativity, memory, and learning. Together, these qualitative lines of study, which examine subjective, emotional, and theoretical aspects, give critical insights that assist in optimising AI music production for human inspiration rather than hindrance, as well as guide future research on the neurology of its reception. The findings also spark a robust multidisciplinary discussion on the ethical trajectories of AI art and its connection to human nature.

Action/Impact The qualitative research's deep insights into the subjective experience and theoretical mechanisms might influence meaningful breakthroughs in AI music technology as well as knowledge frameworks that guide its ethical growth. By identifying inspiration triggers and frustration variables in human-AI co-creation, systems may be iteratively enhanced to foster rather than decrease human uniqueness and expression. Survey-based demographic trends might drive targeted accessibility improvements. The broader music business may use the phenomenological essence abstracted from interviews to create balanced AI-human creative pipelines. Listeners can find fresh experimental compositions near the crossing. As AI music progresses from technical novelty to resonance with the human spirit, this study will provide a philosophically grounded understanding of its relational contours with creators and listeners, paving the way for symbiotic collaboration and ethical responsibility by elevating human-centred subjective insights. The illumination of both possibilities and philosophical cautions can guide discussion, policy, and adoption to steer AI music's action and impact towards positive transformation rather than disruption across industries and communities affected by this evolving interweaving of coded algorithms and creative consciousness.

References

Hutson, M. (2022). AI-generated music poses an ethical quandary. Science, 377(6602), 246-248.

Huang, C. Z., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., ... & Eck, D. (2022). Anthropic: AI Safety through Debate. arXiv preprint arXiv:2210.14952.

Saari, P., Fazlollahi, M., Bourguignon, M., de Nooijer, J. A., Västfjäll, D., & Friberg, A. (2022). Emotions Evoked by Human-Composed vs. Machine-Composed Music. Frontiers in Digital Humanities, 9.

ABSTRACT. In this paper, I show how youth with no musical background, learned to make music using a Digital Audio Workstation (DAW) while engaging in music-based peacebuilding in a post-conflict region. In the late 1980s, communities that were devastated during the Salvadoran civil war (1980-1992) in northern Chalatenango were repopulated after survivors spent nearly a decade living in refugee camps. Today, the descendants of survivors face scant opportunities to heal intergenerational trauma, and to engage in music making. As a member of a large interdisciplinary research team, I attended several meetings and assemblies in the fall of 2022, where community leaders and youth expressed the need for music education. I worked with a team of local youth leaders to design a series of music workshops that focused on peacebuilding through critical dialogue and historical memory work. The project design followed Participatory Action Research (PAR) principles. It was crucial that youth brought the musics they listen to. After much discussion and careful consideration, we identified pop and reggaeton as the main influences of participants. This music is made almost exclusively using the DAW, so we chose a DAW environment to make music. This paper presents preliminary findings after six weeks of in-person workshops that aimed to engage in music-based peacebuilding while making music in a DAW. Data was collected using group dialogue, participant observation, and song analysis. I paid special attention to the process of making music. Participants showed engagement in several informal learning strategies: tacit learning, trial-and-error, and peer collaboration.

ABSTRACT. Previous research has indicated that students actively participate in music learning when their musical preferences are acknowledged; often, it is well-known popular music. In contrast, the previously established Western canon of classical art music tends to alienate students from musical learning in a classroom setting; however, according to the National Curriculum for Music (DfE, 2013), students should acquire a rich and well-rounded curriculum that covers a range of historical styles and genres, including great composers and musicians. As well as the Music General Certificate Secondary School (GCSE) subject content (DfE, 2015) states that at least one area of study should be from the Western Classical Tradition. Thus far, research has not yet examined employing students' musical preferences of popular music in learning other less known, less familiar music genres, for example, classical art music. Moreover, there seems to be a gap in research on how and if classical art music is delivered in secondary music classrooms.

This paper aims to examine the theoretical framework of studying the unknown through the known, extracted from Boardman's (1988) Generative Theory of Musical Learning and applied through the lens of Hess's (2015) Comparative Music Model to provide a more comprehensive picture of the musical genres' interconnection, which could benefit students' engagement with lifelong learning or further music studies, such as Music GCSE. The aim is to highlight the interrelation of different music, where classical art and popular music are examples of seemingly opposite musical genres.

This investigation employs a qualitative grounded theory approach, utilizing several methods. The methodology section is divided into three studies. Each study aims to inform the next stage of research. Study (1) adopts a concurrent embedded multistrategy approach focusing on semi-structured interviews with twelve secondary music teachers about their curriculum and teaching practises of different genres and surveying thirteen music GCSE students regarding their music education. Study (2) will be a pilot participatory case study with a smaller sample testing combined frameworks of Boardman (1989) and Hess (2015), employing the field notes method. Study (3) will be a series of participatory case studies in one secondary school. Data aims to identify students' perception of the teaching and learning of unknown classical art through known popular music. This thesis will explore how learning the unknown through the known can benefit students in broadening their musical knowledge while not failing to recognise their musical preferences, suggesting a method for music teachers to deliver less popular music genres in a music classroom with students of all abilities. I will present preliminary data results and discuss the currently ongoing data collection of the main study for the research.

ABSTRACT. INTRODUCTION

Musical rhythms vary across cultures, featuring distinct characteristics from each culture. Non-isochronous meters (NI), more prevalent in some cultures, involve combinations of long and short beats or groups of beats. Previous studies have concluded that Isochronous meters (IS) are recognised better than NI, short-passive exposure doesn't alter recognition of NI in unfamiliar adults, and musicians are better at recognising IS and NI compared to non-musicians. However, these differences between IS and NI have been studied in fast-tempo and short aksak-type patterns, not the slower and longer patterns prevalent in North Indian Classical Music (NICM), which this study targets. Hence, this study draws insights into the similarity perception of non-isochronous (NI) meters (prevalent in various cultures) compared to isochronous (IS) meters, specifically focusing on longer patterns found in North Indian Classical Music (NICM).

AIMS

The research aims to examine the effect of the length of the cycle on the similarity perception of IS and NI metres among 4 groups of participants: Culturally Familiar Musicians, Culturally Familiar Non-Musicians, Culturally Unfamiliar Musicians and Culturally Unfamiliar Non-Musicians.

It also assesses the impact of short-term explicit learning on the accuracy of similarity-perception of these meters.

Furthermore, it elucidates how cultural familiarity and musical expertise influence the perception of long-IS and shorter-NI meters.

METHODS

A total of 140 participants, including 76 Culturally Familiar (to NICM) participants (32 musicians, 44 non-musicians) and 64 Culturally Unfamiliar participants (34 musicians, 30 non-musicians), were involved in the study. Stimuli comprised of original stimuli (rhythms: 16-beat IS-Teental and 7-beat NI-Rupaktal from NICM) and test stimuli (rhythms) which are metrical and altered in structure. The experiment was divided into baseline, training/short-term learning, and testing phases. The baseline and Testing phases involved participants rating the similarity of test stimuli to original stimuli, and the short-term learning included explicit instructional videos for original rhythms.

RESULTS

Contrary to previous findings, this study reveals that the shorter-NI meter was rated more accurately than the longer-IS meter by both culturally familiar and culturally unfamiliar participants. Additionally, culturally familiar musicians exhibited superior accuracy in the similarity rating of NI-Rupaktal, but not of IS-Teental. This suggests an important effect of the length of the metric cycle in its similarity perception.

Explicit short-term learning led to an improvement in the accuracy of similarity ratings of shorter-NI meters among culturally unfamiliar musicians and culturally familiar non-musicians.

Furthermore, only culturally familiar musicians were able to differentiate the altered meter significantly for NI-Rupaktal, suggesting that culture-specific musical expertise and not just musicianship or familiarity affects the perception of NI in NICM.

CONCLUSION

These findings emphasise on the importance of the length of the rhythmic cycle of IS or NI in meter perception. It also contributes to our understanding of cross-cultural differences in rhythm perception and underscores the importance of culture-specific musical expertise in the similarity perception of IS and NI meters.

ABSTRACT. The Covid Pandemic impacted music education like no other discipline, given the unique nature and reliance on acoustic sound within the room when teaching face-to-face, and the accepted teacher-student proximity within the learning environment (studio or classroom). The past three years necessitated the imperative for teachers to foster creative pedagogies and refine pedagogies to sustain student connection and learning, while maintaining curriculum delivery and assessment. This presentation tables the experiences and insights of two tertiary teachers as they delivered a blended performance teaching course to student in a master’s program in Australia, with a high dependence on diverse technologies, including: Zoom, LMS, Online resources and assessment combined with challenges associated with latency, download speed and access of resources.

Through a series of self-study scenarios and lived experiences, the ‘learnings’ and ‘musings’ that were created to meet the needs of students are presented. Redefined pedagogies are viewed through the need to be present through an ‘authentic’ teaching and learning environment, one that reflects the world and the music industry at present. Drawing on data collected over an 18-month period and analysed purposefully through a dual, research in practice ‘lens’, key findings are shared, illustrated, and discussed. Of key importance and interest are the illustrations of practice that emerged, highlighting the need for adaptive and sustained pedagogical shifts within music instruction, including re-visioned integration of digital technologies (local and online), intersection of multiple tools in the learning process, media convergence and lesson design, coupled with pedagogies for ‘live’ and ‘online’ students being taught simultaneously. The power of collaborative teacher knowledge and refinement of teaching capacity through ongoing reflections, musings and interaction were outcomes that ensured quality music teaching in performance was sustained and students were engaged. This presentation concludes by emphasising the need for practice and research to reflect the needs of society and the ‘real-world’ as education observes an increased level of digitally dependence post-pandemic. The value and importance of undertaking co-operative research to inform the future of teaching, learning and technology integration across diverse genres and styles is reinforced.

ABSTRACT. Feedback is an important tool to support student learning. The use of technology tools, such as asynchronous video recordings, can be used by music tuition instructors to convey their assessment feedback to students through audio and graphic means. This means instructors can record themselves giving both verbal feedback and model various part of music repertoire or technique aspects. Students view their instructor’s video feedback multiple times, as needed, for key musical details (i.e., musical embellishments, tone, technical details, etc.). Within online teaching formats, the use of teacher video feedback becomes an integral part of the learning process. Teacher use of video feedback, or multimodal feedback, as a response to student assessment has potential to provide clearer feedback than text, support strengthening of teacher-student connection and provide detailed context of voice tone, modelling, and music performance nuances. The limited exploration on the efficacy of video feedback when learning to play an instrument or voice calls for further research. Hattie and Timperley (2007, p. 86) identify three major feedback questions: ‘Where am I going?’; ‘How am I going?’ and; ‘Where to next?’. Building on how teachers can explore feedback beyond text, Froelich and Guias (2021) identify four process steps for providing feedback: “1. Inspecting the feedback object, 2. Spotting topics to give feedback about, 3. Prioritising and filtering these topics for writing it down, and 4. Disseminating the feedback.” (p. 2) and note that steps 1 to 3 are often not normally visible to the receiver in written forms of feedback. This is considerably important within the context of music instruction. By providing video feedback to music performance students, the instructor can report on the correctness of a particular task and the use of multimodal feedback extends the opportunity to talk through the process of improvement in ‘real time’ and in more detail, similar to a face-to-face meeting. Of significance for music education is the identification of effective teaching strategies for implementing video feedback and video feedback loops for students learning to play a musical instrument. The feedback model “Formative Video Feedback in Music Performance Instruction” (Blackburn & Johnson, 2023) developed through teacher-as-researcher and literature review aims to provide an initial exemplar toward a strategic cycle for the implementation of teacher’s video feedback in music performance teaching assessments. This presentation will explore the opportunities and challenges of the model, how it can be implemented in higher education music performance tuition, and recommendations for future research.

References Blackburn, A. & Johnson, C. (2023). A Model for Formative Video Feedback in Music Performance Teaching. Australasian Society of Computers in Learning in Tertiary Education TELall blog. https://blog.ascilite.org/a-model-for-formative-video-feedback-in-music-performance-instruction/ Froelich, D.E., & Guias, D. (2021). Multimodal Video-Feedback: A Promising way of Giving Feedback on Student Research. Frontiers in Education: Curriculum, Instruction, and Pedagogy 6(763203). https://doi.org/10.3389/feduc.2021.763203 Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487

ABSTRACT. In this article, we explore the integration of artificial intelligence (AI) in music, considering its potential applications in music education. Firstly, we carry out a categorisation to identify how AI can be used in areas such as creation, composition, production, mixing, mastering and music education. In a near future, we will begin a state-of-the-art study to see how far the literature discusses the use of these tools in music education, and then we will carry out experiments to see how these technologies can be used in different music education environments.

Introduction

In order to understand Artificial Intelligence (AI), it is necessary to unravel the terms "intelligence" and "artificial". Intelligence is often seen as a structured space rich in diverse information processing capabilities, involving psychological skills such as perception, association, prediction, planning and motor control (Ribeiro and Almeida, 2005). On the other hand, artificiality refers to human creation, i.e. elements that do not originate in nature but are created by human beings (Marques and Krüger, 2019).

Artificial Intelligence is the convergence of these concepts, where "artificiality" can be usead as a means to encapsulate and emulate "intelligence" in machines (Copeland, 2023). On the other hand, Nilsson (1998) defines AI as:

Artificial Intelligence (AI), broadly defined (and somewhat circularly), relates to intelligent behaviour in artefacts. Intelligent behaviour, in turn, involves perception, reasoning, learning, communication and action in complex environments. One of the long-term goals of AI is the development of machines that can do these things as well as humans, or possibly even better (Nilsson, 1998, p 24).

It can be said that the application of AI in music is becoming increasingly diverse and sophisticated, ranging from assisted composition tools to advanced mixing and mastering systems. This integration could represent a breakthrough for music teaching in several instances. The use of digital technologies contributes to a growing increase in the variety of methodological options and proposals for teaching music. All this technology, amalgamated in a collaborative methodological proposal, can contribute considerably to the knowledge and musical development of students and teachers. (Cernev, 2018, p.12).

This article aims to categorise and present an overview of the various applications of AI in music. We seek to identify and describe the AI tools that are shaping contemporary music creation, production, analysis and education. Through this categorisation, we aim to provide an insight into the current and future possibilities that AI can offer in music and more specifically in music education.

Categorisation

Advances in AI technologies have led to the development of a variety of software and hardware for musicians, producers and music educators. These tools include, but are not limited to, Digital Audio Workstation (DAW) software with AI functionalities, music content recommendation systems, skills training applications, AI-assisted composition platforms, among others.

Based on Ribeiro and Marins' (2023) critical analysis of the use of Artificial Intelligence tools in music, we propose the following categorisation:

- AI Composition: Tools that help you create music, either by composing entirely new songs or by assisting with music production. Examples: Amper Music and Voicemod.

- AI generation: Tools that generate music automatically, based on user specifications. Examples: Musenet, Jukebox.

- AI Production: Tools focused on specific aspects of music production, such as adding depth to audio or improving sound quality. Examples: LANDR, Izotope RX 10.

- Music Practice Tools: Tools designed to improve specific skills, such as music theory, ear training and rhythm practice, often in a playful way. Examples: Moises, and Duolingo Music.

- DAW AI (Digital Audio Workstation): Tools that integrate AI functionalities into a digital audio workstation, enabling advanced functions such as text-to-music conversion. Examples: WavTool AI and BandLab.

- Suite of Creative Tools: Sets of tools and plugins that offer various functionalities to enhance the musical creative process. Examples: Magenta Studio and Orb Producer Suite 3.

- Learning Platforms: Tools designed to teach musical skills, such as instruments and music theory, through interactions and personalised feedback. Examples: Yousician and SimplyPiano.

- Virtual Tutoring: applications that simulate tutoring in music teaching, offering the opportunity to focus on adaptive and customisable content. Examples include Gemini, ChatGpt and Perplexity, which are not designed for music, but are used in music applications.

It is also worth mentioning that list of apps and software are not limited to those presented above. Also, many of these tools can lay in different categories.

The inclusion of Artificial Intelligence in music education may be redefining the way music is taught and learnt, and bringing advances to music education. It is important to emphasise that these tools still need to be researched and tested in different educational environments.

In this text, we propose a categorisation of Artificial Intelligence tools. The next step will be a state-of-the-art study to see how far the literature discusses the use of such tools in music education. Experiments will then be carried out with these tools in a variety of music education settings. It is hoped that the results of this research will bring advances and proposals regarding the use of artificial intelligence in music education.

References

CERNEV, Francine Kemmer. Collaborative Music Learning Mediated by Digital Technologies: A Methodological Proposal for Music Teaching. Journal of the Brazilian Association of Music Education (ABEM), v. 26, n. 40, 2018. Available at: https://revistaabem.abem.mus.br/revistaabem/article/view/718.

COPELAND, B.J. Artificial intelligence. In: ENCYCLOPEDIA BRITANNICA, 2023. Available at: https://www.britannica.com/technology/artificial-intelligence.

NILSSON, N. J. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann Publishers, 1998.

RIBEIRO, Iolanda da Silva; ALMEIDA, Leandro S. Information processing speed in the definition and evaluation of intelligence. Psicologia: Teoria e Pesquisa, Brasília, v. 21, n. 1, p. 43-49, jan./abr. 2005. Available at: https://www.scielo.br/j/ptp/a/qT7XmWymnQGcdzTX8nj5Vkd/?lang=pt&format=html.

RIBEIRO, T. C.; MARINS, P. R. A. Artificial Intelligence applied to musical training processes: ethical questions about a creative symbiosis. In: Proceedings of Nas Nuvens Congress, 2023, Minas Gerais. 9th Nas Nuvens Music Congress, 2023.

ABSTRACT. On December 11, 2023, Canada’s Western University held its first Q&A session on the impact of artificial intelligence (AI) in academia. Western’s Chief AI Officer Mark Daley started the session by asking a question, “Use one word to describe AI.” Among all the audiences’ answers shown on the screen through the real-time interaction, the words “exciting” and “scary” were most frequently used. Such mixed feelings about AI manifested in how people have been perceiving the boost of AI programs such as ChatGPT since late 2022. As Generative AI models, ChatGPT, along with its siblings DALL·E and Sora, have been showing their capacities to generate creative content in writing, computer programming, visual arts, and videos. Such emergence of Generative AI models impacts human beings’ work, life, and study. For example, Franchi (2013) discusses how AI tools may change composers’ working habits through their human-machine working interaction. On the other hand, Generative AI’s comprehensive creative capacities raise people’s concerns about the possibility that AI outperforms and even replaces human composers (Bown, 2021; Gioti, 2020; Louth, 2015).

AI has the potential to be incorporated into a lot of aspects of human lives, which causes people’s excitement and concerns about our future. This technological advancement necessitates a critical philosophical inquiry into AI’s role in our day-to-day lives and work. As Bowman and Frega (2012a) argue, such inquiry “often challenges and subverts habitual thought processes, processes that are familiar, reassuring, and consoling. Philosophical inquiry works, when and if it does, by generating conceptual tensions that may initially involve confusion and discomfort” (pp. 5-6). As a field that is deeply associated with social, cultural, and political systems rather than an isolated subject (Bowman & Frega, 2012a), music education is also facing philosophical reflections on rethinking the roles of AI in theory and praxis in music education. How do music educators philosophically consider the role of AI in mediating the ways in which teachers and learners engage in music learning? This presentation will review philosophical inquiries in AI and music education in the aspects of praxis, subjectivity, and empowerment. By exploring the ways that AI addresses some philosophical issues that may have lacked enough praxis and attention in music education, music educators can have the opportunity to reflect on their musical engagement with their students, thus refining their teaching capabilities for a better, creative, and sustainable future (Bowman & Frega, 2012b; Morton, 2012).

ABSTRACT. Background There is a gap in music performance, education and psychology in terms of memorization training for post-tonal piano music. Despite the repertoire spanning over 100 years, pedagogues and professionals still lack effective tools for developing this skill (Soares, 2015). Existing research on this domain is mostly focused on observing practitioners’ behaviors during practice, to understand how these prepare for a memorized performance of a selected repertoire (Chaffin et al., 2010; Fonte, 2020). However, the resulting Performance Cue Theory that emerges from these studies does not provide a systematic method to assist learning, but instead, explains performers’ behaviors to fulfil the given task (Chaffin et al., 2002; Farré Rozada, 2023). Furthermore, other important aspects of memorization, such as the role of sleep for memory consolidation; influential parameters of performance practice, such as the abilities of perfect pitch and sight-reading; or the role of emotions have rarely been examined or simply omitted.

Aims This spoken paper focuses on the results of testing, extending and formalizing a new method for analysis, learning and memorization of post-tonal piano music, named Conceptual Simplification (Farré Rozada, 2023). This presents a novel implementation to musical memorization of group theory, number theory and geometry; and of computer-science paradigms used to optimize algorithm design. Therefore, it builds on mathematics and computer science to improve human memory and musical performance. However, as demonstrated, Conceptual Simplification does not require any previous scientific training to be successfully implemented and works for different learning styles and types of complexity.

Method Conceptual Simplification is tested through a series of studies with practitioners, who range from conservatoire piano students to international performers, including observation and analysis of the author’s own performing practice. The repertoire featured involves existing post-tonal and commissioned works.

Results From testing the parameters of perfect pitch, synesthesia, sight-reading, emotions, sleep, mental practice, complexity and expertise; the most influential parameters for memorization identified are perfect pitch, sight-reading, sleep and complexity. Additionally, a formal definition for complexity is formulated. Similarly, after testing different practice and performance strategies, the most effective strategies for memorization identified are simplifying strategies and conceptual encoding strategies, included in Conceptual Simplification. Finally, it is also revealed the positive role of mental practice for coping with performance anxiety and self-sabotage.

Conclusions Although the scope of these studies was limited to testing Conceptual Simplification for post-tonal piano music, this method could be adapted to other instrumentalists, singers and conductors; and musical genres. More ambitious applications might involve non-musical domains, since Conceptual Simplification essentially scaffolds complexity, proceeding in a non-linear manner and avoiding time-consuming procedures. The method also presents enough flexibility for other practitioners to incorporate additional strategies, adapting it to their needs accordingly. Finally, Conceptual Simplification also indicates promising additional benefits. Concretely, in preventing performance anxiety through greater confidence and reducing the potential for injuries that usually result from repeated practice. Conceptual Simplification’s systematic approach toward engaging conceptual memory and reasoning leads to more confident memorized performances, while needing less repetition during practice.

References Chaffin, R., Imreh, G. and Crawford, M. (2002) Practicing Perfection: Memory and Piano Performance. New Jersey: Erlbaum.

Chaffin, R., Lisboa, T., Logan, T. and Begosh, K. (2010) Preparing for memorized cello performance: the role of performance cues. Psychology of Music, 38(1), pp. 3–30.

Farré Rozada, L. (2023) Conceptual Simplification: an Empirical Investigation of a New Method for Analysis, Learning and Memorisation of Post-Tonal Piano Music. PhD Thesis. Royal Birmingham Conservatoire.

Fonte, V. (2020) Reconsidering Memorisation in the Context of Non-Tonal Piano Music. PhD Thesis. Royal College of Music, London. Retrieved on 24 April 2022 from https://doi.org/10.24379/RCM.00001619

Soares, A. (2015) Memorisation of Atonal Music. DMus Thesis. Guildhall School of Music and Drama, London. Retrieved on 06 December 2019 from http://openaccess.city.ac.uk/id/eprint/15964/

ABSTRACT. In the UK, Education, and, consequently, music education, appear to be at a neonatal stage when it comes to addressing global climate change and sustainability issues. Global metrics suggest that this situation appears to be as challenging, if not worse, around the world. In this paper, we will be offering a general taxonomic map of issues that are central to a future sustainable music educational praxis. This is the result of a comprehensive review of key literatures in the overarching field. Additionally, we will be offering information about a novel UCL initiative, the "Crafting Sustainabilities Collective", a creative synergies group connected by the common goal to co-design new interventions with groups and communities that harness the possibilities of creative making involving technology in support of sustainability.