Embedding Observation in Audio Education Software: A Preliminary Perspective

Garland K, Ronan M, Bassett M. Embedding Observation in Audio Education Software: A Preliminary Perspective. In: AES Conference: AES 2023 International Conference on Audio Education. Audio Engineering Society; 2023. Paper 4. Available from: https://aes.org/publications/elibrary-page/?id=22226

@inproceedings{Garland2023_22226,
  author = {Garland, Kevin and Ronan, Malachy and Bassett, Mark},
  title = {{Embedding Observation in Audio Education Software: A Preliminary Perspective}},
  booktitle = {AES Conference: AES 2023 International Conference on Audio Education},
  note = {Paper 4},
  year = {2023},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22226}
}

TY  - CPAPER
TI  - Embedding Observation in Audio Education Software: A Preliminary Perspective
AU  - Garland, Kevin
AU  - Ronan, Malachy
AU  - Bassett, Mark
T2  - AES Conference: AES 2023 International Conference on Audio Education
M1  - Paper 4
PY  - 2023
DA  - 2023/09/06
UR  - https://aes.org/publications/elibrary-page/?id=22226
PB  - Audio Engineering Society
LA  - en
AB  - Observation and physical recall of expert interactions plays a key role in assimilating knowledge of audio technology workflows and practices, and is a main component of development within traditional apprenticeship pathways. Comparatively, higher-level audio education (AE) programmes may lack the resources for extensive implementation of such approaches, instead relying upon developing mixing subskills within situationally representative software and assistive educational technologies. This constraint presents a unique opportunity for the realm of educational mixing technologies, which can utilize observational learning methods to relay contextual audio processing procedures. This paper theorizes embedding observational learning within ear training paradigms to potentially facilitate procedural and behavioral learning effects in the context of audio effect processing. Furthermore, this work acts as a preliminary investigation of embedding expert models into educational mixing technologies.
ER  -