Automatic transcription of polyphonic music remains a challenging task in the field of Music Information Retrieval. In this paper, we propose a new method to post-process the output of a multi-pitch detection model using recurrent neural networks. In particular, we compare the use of a fixed sample rate against a meter-constrained time step on a piano performance audio dataset. The metric ground truth is estimated using automatic symbolic alignment, which we make available for further study. We show that using musically relevant time steps improves system performance despite the choice of a basic representation, although mostly because it quantises the output durations. This is an encouraging result for further investigation of musically-motivated neural network designs