@samaaron busy as you no doubt are Sam, you might be interested to know that I am participating in a research project being run by the Open University, into creative improvisation in instrumental tuition, and I will be using one of my Sonic Pi students as a ‘case study’ (horrible term!). I have searched online to find papers/articles you have written or contributed to on the topic of Sonic Pi in the classroom, and web results have been a bit patchy. If you have sec could you - or anyone else reading - point me to your own publications list please.
Hello. So, I ran your request in Perplexity which is an AI search aggregator. I then supplemented the initial retrieved sources with results from google scholar and github. I broke the thread up into four sections, but it’s all contained within one thread. Feel free to add queries or source. Otherwise you don’t need an account to view the results. Good luck!
Sure. Curious, what is the specific nature of this research project? I find it fascinating that this aspect of musical performance is actively being taught such as it is, as the methods I developed later in life weren’t particularly emphasized in my early education. And by methods, I mean, learning basic music theory, where the notes are on the guitar, then putting my headphones on and allowing myself endless time to just build the physical habits of finding the sound while ignoring conventional practice as I found later in life that conventional methods of learning music, are not the best. I listened to mostly to groups who primarily improvise with a jazz influence ( I won’t mention names to reduce bias) and then also edm/hip-hop which greatly helped to expand on polyrhythmic/syncopated improv.
Anyway, today I added the source “Jamming the classroom: Musical improvisation and pedagogical practice” as it…“examines autodidactic methods of learning to improvise, including the role of influences and the development of an expanded musical vocabulary…”
Summary:
Autodidactic Methods of Learning to Improvise
Autodidacticism, or self-directed learning, is a critical aspect of mastering improvisational skills in music. The process of teaching oneself to improvise involves a combination of personal exploration, experimentation, and the absorption of influences from a variety of sources. Here’s an expansion on autodidactic methods of learning to improvise, as discussed in the provided source:
Self-Directed Exploration
Musicians often engage in self-directed exploration, where they spend time alone with their instrument, experimenting with different sounds, techniques, and musical ideas. This solitary practice allows for a deep, personal connection with the instrument and the development of a unique musical voice.
Learning from Recordings
Listening to and learning from recordings is a significant part of autodidactic learning. Musicians study the works of others, transcribe solos, and analyze different styles to incorporate new elements into their playing. This process helps in expanding their musical vocabulary and understanding of improvisational techniques.
Imitate to Innovate
The “imitate to innovate” approach involves emulating the styles and techniques of admired musicians as a starting point for developing one’s own style. By imitating, musicians internalize various aspects of music-making, which they can later adapt and transform into something personal and innovative.
Experimentation and Imagination
Experimentation is key to discovering new sounds and techniques on an instrument. Musicians often imagine new sonic possibilities and then work to realize these ideas through experimentation. This process can lead to the development of extended techniques and a broader musical vocabulary.
Technical Proficiency and Practice
Technical proficiency is essential for realizing musical ideas during improvisation. Musicians engage in technical practice to maintain their skills and expand their range of expression. This includes exercises for dexterity, tone production, and control over various musical parameters.
Unlearning and Relearning
Part of the autodidactic process may involve unlearning certain habits or preconceptions to make way for new approaches to music. This can be a challenging but rewarding process that allows musicians to approach their craft with fresh perspectives.
Conceptual Unlearning
Those with a background in traditional music may need to “unlearn” certain concepts to fully embrace the possibilities of music coding. This might involve rethinking the relationship between musical input and output, as well as embracing the non-linear and generative aspects of coding music.
@book{heble2023jamming,
title={Jamming the classroom: Musical improvisation and pedagogical practice},
author={Heble, Ajay and Stewart, Jesse},
year={2023},
publisher={University of Michigan Press}
}
Hi
thanks for the detailed response. In answer to your opening question: the nature of the project is to determine if and how independent creativity and improvisation are taught in instrumental lessons, and the school I teach in is involved in broad and early case studies (polling tutors and students, and identifying early research topics). The study is in its infancy currently, but will definitely be pursued to its conclusion.
The 7 points you raise are important, and I look forward to reading in more detail; that looks like a great, and highly relevant reference.
[edit] Forgive my ignorance, are you one of the authors @b08x ?
Just gonna jump in here to point out that several of the results given from the query “what are some of the key concepts covered in the currdev_unit_plan-sonic-pi-gen-music unit?” are not accurate, for example, the currdev_unit_plan-sonic-pi-gen-music unit does not include anything about implementing “backends for algorithmic music generation, such as Markov chains and Magenta’s MusicRNN chord_pitches_improv checkpoint” or “showcasing how students can utilize the Open Sound Control protocol to connect Sonic Pi with external tools for music creation.”
I say this as the author of the currdev_unit_plan-sonic-pi-gen-music unit.
Just a cautionary tale of how AI can sometimes come up short with providing the information you might be looking for.
