Computational Ethnomusicology

Abstract

John Blacking said “The main task of ethnomusicology is to explain music and music making with reference to the social, but in terms of the musical factors involved in performance and appreciation” (1979:10). For this reason, research in ethnomusicology has, from the beginning, involved analysis of sound, mostly in the form of transcriptions done “by ear” by trained scholars. Bartók’s many transcriptions of folk music of his native Hungary are a notable example. Since the days of Charles Seeger, there have been many attempts to facilitate this analysis using various technological tools. We survey such existing work, outline some guidelines for scholars interested in working in this area, and describe some of our initial research ef- forts in this field. We will use the term “Com- putational Ethnomusicology” (CE) to refer to the design, development and usage of com- puter tools that have the potential to assist in ethnomusicological research. Although not new, CE is not an established term and exist- ing work is scattered among the different dis- ciplines involved. As we quickly enter an era in which all recorded media will be “online,” meaning that it will be instantaneously available in digital form anywhere in the world that has an Inter- net connection, there is an unprecedented need for navigational/analytical methods that were entirely theoretical just a decade ago. This era of instantaneously available, enormous collec- tions of music only intensifies the need for the tools that fall under the CE rubric. We will concentrate on the usefulness of a relatively new area of research in music called Music Information Retrieval (MIR). MIR is about designing and building tools that help us organize, understand and search large collec- tions of music, and it is a field that has been rapidly evolving over the past few years, thanks in part to recent advances in computing power and digital music distribution. It en- compasses a wide variety of ideas, algorithms, tools, and systems that have been proposed to handle the increasingly large and varied amounts of musical data available digitally. Researchers in this emerging field come from many different backgrounds including com- puter science, electrical engineering, library and information science, music, and psychology. The technology of MIR is ripe to be integrated into the practice of ethnomusicological research. To date, the majority of existing work in MIR has focused on either popular music with applications such as music recommendation systems, or on Western “classical” music with applications such as score following and query-by- humming. In addition, as microchips become smaller and faster and as sensor technology and ac- tuators become cheaper and more precise, we are beginning to see ethnomusicological re- search incorporating both robotic systems and digital capture of music-related bodily ges- tures; music in general is embodied and in- volves more than a microphone can record. Our hope is that the material in this paper will help motivate more interdisciplinary and mul- tidisciplinary researchers and scholars to ex- plore these possibilities and solidify the field of computational ethnomusicology.