  Instrument separation in an ensemble is a challenging task. In this work, we
address the problem of separating the percussive voices in the taniavartanam
segments of Carnatic music. In taniavartanam, a number of percussive
instruments play together or in tandem. Separation of instruments in regions
where only one percussion is present leads to interference and artifacts at the
output, as source separation algorithms assume the presence of multiple
percussive voices throughout the audio segment. We prevent this by first
subjecting the taniavartanam to diarization. This process results in
homogeneous clusters consisting of segments of either a single voice or
multiple voices. A cluster of segments with multiple voices is identified using
the Gaussian mixture model (GMM), which is then subjected to source separation.
A deep recurrent neural network (DRNN) based approach is used to separate the
multiple instrument segments. The effectiveness of the proposed system is
evaluated on a standard Carnatic music dataset. The proposed approach provides
close-to-oracle performance for non-overlapping segments and a significant
improvement over traditional separation schemes.
