TY - GEN
T1 - Embedding Observation in Audio Education Software
T2 - AES International Conference on Audio Education 2023
AU - Garland, Kevin
AU - Ronan, Malachy
AU - Bassett, Mark
N1 - Publisher Copyright:
© 2023 AES International Conference on Audio Education 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85196729390&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85196729390
T3 - AES International Conference on Audio Education 2023
SP - 32
EP - 41
BT - AES International Conference on Audio Education 2023
PB - Audio Engineering Society
Y2 - 6 September 2023 through 8 September 2023
ER -