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Sema – A Playground for Live Coding Music and Machine Learning

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Sema is a playground where you can rapidly prototype live coding mini-languages for signal synthesis, machine learning and machine listening.

Sema aims to provide an online integrated environment for designing both abstract high-level languages and more powerful low-level languages.

Sema implements a set of core design principles:

Dependencies

Sema requires the following dependencies to be installed:

In order to run, Sema must connect with a project url and api key to a Supabase backend.

How to build and run the Sema playground on your machine

If you decide to use npm to build sema, you can follow this list of commands:

$ cd sema
$ npm install
$ npm run build
$ npm run dev

If you decide to go with the Yarn package manager instead, you can use the following list of commands:

To use Yarn:

$ cd sema
$ yarn
$ yarn build
$ yarn dev

Once you have sema running as a node application, you can load it on your browser on the following ports

Hardware acceleration:

Hardware acceleration will have a drastic effect in Tensorflow.js model training speed.

To enable it in Chrome:

To enable in Firefox:

Linux Users

Sema uses Web Audio API Audio Worklets. Their performance seems very sensitive to CPU power scaling. If you are experiencing sound quality issues, try setting the CPU governor to performance mode. e.g on Ubuntu,

$ cpupower frequency-set --governor performance

Documentation

Sema's internal documentation aims at supporting the users learning experience. It is integrated within the application and comprises the following sections:

Sema's Wiki documentation aims at supporting contributions. It focuses on how Sema is designed and built:

Contributing

Sema is an open-source project and hopefully the underlying vision, aims and structure will motivate you to contribute to it. Check the following:

Publications

Bernardo, F., Kiefer, C., Magnusson, T. (2021). Assessing the Support for Creativity of a Playground for Live Coding Machine Learning, In: Baalsrud Hauge J., C. S. Cardoso J., Roque L., Gonzalez-Calero P.A. (eds) Entertainment Computing – ICEC 2021. ICEC 2021. Lecture Notes in Computer Science, vol 13056. Springer, Cham. https://doi.org/10.1007/978-3-030-89394-1_38

Bernardo, F., Kiefer, C., Magnusson, T. (2020). A Signal Engine for a Live Coding Language Ecosystem, J. Audio Eng. Soc., vol. 68, no. 10, pp. 756-766. doi: https://doi.org/10.17743/jaes.2020.0016

Bernardo, F., Kiefer, C., Magnusson, T. (2020). Designing for a Pluralist and User-Friendly Live Code Language Ecosystem with Sema. 5th International Conference on Live Coding, University of Limerick, Limerick, Ireland

Bernardo, F., Kiefer, C., Magnusson, T. (2019). An AudioWorklet-based Signal Engine for a Live Coding Language Ecosystem. In Proceedings of Web Audio Conference 2019, Norwegian University of Science and Technology (NTNU), Trondheim, Norway (Best Paper Award at Web Audio Conference 2019)