Polyphonic Music Sequence Transduction with Meter-Constrained LSTM Networks

Abstract

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.