  The tabla is a unique percussion instrument due to the combined harmonic and
percussive nature of its timbre, and the contrasting harmonic frequency ranges
of its two drums. This allows a tabla player to uniquely emphasize parts of the
rhythmic cycle (theka) in order to mark the salient positions. An analysis of
the loudness dynamics and timing deviations at various cycle positions is an
important part of musicological studies on the expressivity in tabla
accompaniment. To achieve this at a corpus-level, and not restrict it to the
few recordings that manual annotation can afford, it is helpful to have access
to an automatic tabla transcription system. Although a few systems have been
built by training models on labeled tabla strokes, the achieved accuracy does
not necessarily carry over to unseen instruments. In this article, we report
our work towards building an instrument-independent stroke classification
system for accompaniment tabla based on the more easily available tabla solo
audio tracks. We present acoustic features that capture the distinctive
characteristics of tabla strokes and build an automatic system to predict the
label as one of a reduced, but musicologically motivated, target set of four
stroke categories. To address the lack of sufficient labeled training data, we
turn to common data augmentation methods and find the use of pitch-shifting
based augmentation to be most promising. We then analyse the important features
and highlight the problem of their instrument-dependence while motivating the
use of more task-specific data augmentation strategies to improve the diversity
of training data.
