Gridless Grooves: Cello-Drumming in Indonesian Langgam Jawa

Andy McGraw University of Richmond (USA)

Abstract

At a previous meeting I presented preliminary results from a comparative analysis of microtiming in langgam Jawa (a genre of kroncong) cello-drumming and Javanese ciblon kendang drumming. While cello-drumming is ostensibly based upon ciblon patterns, I identified an apparently opposed tendency in their “grooves”: cello players tended to play ahead of time-keeping instruments (such the cuk ukulele); kendang players tended to play behind time-keeping instruments (such as the peking metallophone). These results were based on a small sample of three kroncong recordings and one gamelan recording. My 2020 presentation will significantly revise these findings based on a larger corpus of new recordings made in Java in 2018. These new findings primarily point to the comparative independence of both cello and ciblon onsets from the near-isochronous grid established by time-keeping instruments. However, rather than characterizing their grooves as “rushing” or “dragging” relative to this grid, it appears to make more sense to characterize them as filling a continuous, gridless, space between goal tones (seleh) in ways distinctive to each instrument.

Background:

Kroncong, an Indonesian string-band music, evolved from the introduction of Western string instruments to the archipelago beginning in the early sixteenth century. As compared to gamelan, the ethnomusicological literature includes few examples of the analysis of Indonesian popular music such as kroncong. Although Yampolsky has analyzed kroncong harmonic structures (1990, 2010), detailed analyses of langgam Jawa remain rare. This presentation redresses the lacunae.

Methodology:

I will analyze cello and ciblon onsets derived from recordings made in Java in 2017 and 2018. These are compared to the onsets of time-keeping instruments in order to characterize the structure of cello and ciblon “groove.” I compare results derived from three recording methods: tracked studio recordings, live sensor/piezo-based recordings, and live close mic-ing. Onset timings are derived using Sonic Visualizer and the resulting data is visualized and analyzed using custom Mathematica scripts. Aside from my principle findings regarding groove, I critically reflect on the analytical consequences of comparing onsets from different instruments types (string and percussion), recording, systems and algorithms. Different methodologies produce nontrivial differences in data for the analysis of microtiming.