Towards end-to-end polyphonic music transcription: Transforming music audio directly to a score

Abstract

We present a neural network model that learns to produce music scores directly from audio signals. Instead of employing commonplace processing steps, such as frequency transform front-ends, harmonicity and scale priors, or temporal pitch smoothing, we show that a neural network can learn such steps on its own when presented with the appropriate training data. We show how such a network can perform monophonic transcription with very high accuracy, and how it also generalizes well to transcribing polyphonic music.