Open AccessSTILL RECORDING AFRICAN MUSIC IN THE FIELD
Author(s) -
Gerhard Roux
Publication year - 2018
Publication title -
african music
Language(s) - English
Resource type - Journals
eISSN - 2524-2741
pISSN - 0065-4019
DOI - 10.21504/amj.v11i1.2296
Subject(s) - metadata , field (mathematics) , computer science , process (computing) , quality (philosophy) , data collection , world wide web , data science , checklist , point (geometry) , action (physics) , sound recording and reproduction , information retrieval , psychology , sociology , social science , philosophy , geometry , mathematics , epistemology , pure mathematics , cognitive psychology , operating system , physics , quantum mechanics , acoustics
Field sound recordings are an indispensable source of data for ethnomusicologists. However, to my knowledge there are no standards or guidelines of how this data should be captured and managed. With the progress made in machine learning, it has become vital to record data in a way that also supports the retrieval of information about the music. This article describes a model developed for field recordings that aims to aid an objective data gathering process. This model, developed through an action research process that spanned multiple field recording sessions from 2009–2015, include recording equipment, production processes, the gathering of metadata as well as intellectual property rights. The core principles identified in this research are that field recording systems should be designed to provide accurate feedback as a means of quality control and should capture and manage metadata without relying on secondary tools. The major findings are presented in the form of a checklist that can serve as a point of departure for ethnomusicologists making field recordings.