An approach to diarize taniavartanam segments of a Carnatic music concert is proposed in this paper. Information bottleneck (IB) based approach used for speaker diarization is applied for this task. IB system initializes the segments to be clustered uniformly with fixed duration. The issue with diarization of percussion instruments in taniavartanam is that the stroke rate varies highly across the segments. It can double or even quadruple within a short duration, thus leading to variable information rate in different segments. To address this issue, the IB system is modified to use the stroke rate information to divide the audio into segments of varying durations. These varying duration segments are then clustered using the IB approach which is then followed by Kullback-Leibler hidden Markov model (KL-HMM) based realignment of the instrument boundaries. Performance of the conventional IB system and the proposed system is evaluated on standard Carnatic music dataset. The proposed technique shows a best case absolute improvement of 8.2% over the conventional IB based system in terms of diarization error rate. © 2018 International Speech Communication Association. All rights reserved.