The estimation of fundamental frequency of instruments is an important task in computational audio analysis. The current state of the art methods use neural networks for this task. This process is typically computed periodically over very short segments of a monophonic audio signal so that minute shifts in intonation can be detected. However, the steelpan has discretely tuned notes where the performer has no direct control over pitch once a note has been activated. The activation of a note has great influence over the acoustical properties of the resultant note. Much research has been devoted to the tonality, construction, and acoustical properties of steelpans, but relatively little focuses on the attack transient specifically.This paper evaluates the application of pitch detection methods to the attack transients of steelpan notes. A dataset containing labeled audio samples from multiple tenor steelpans is used for training and evaluation. The accuracy of this approach for pitch detection is compared with established methods applied to both entire notes and only attack transients. Determining a steelpan note’s pitch from the attack transient is an important first step in building a robust low latency automatic transcription system that can be used for both analysis as well as live performance.