Title: Context-Independent Polyphonic Piano Onset Transcription with an Infinite Training Dataset,
Abstract: Many of the recent approaches to polyphonic piano note onset transcription
require training a machine learning model on a large piano database. However,
such approaches are limited by dataset availability; additional training data
is difficult to produce, and proposed systems often perform poorly on novel
recording conditions. We propose a method to quickly synthesize arbitrary
quantities of training data, avoiding the need for curating large datasets.
Various aspects of piano note dynamics - including nonlinearity of note
signatures with velocity, different articulations, temporal clustering of
onsets, and nonlinear note partial interference - are modeled to match the
characteristics of real pianos. Our method also avoids the disentanglement
problem, a recently noted issue affecting machine-learning based approaches. We
train a feed-forward neural network with two hidden layers on our generated
training data and achieve both good transcription performance on the large MAPS
piano dataset and excellent generalization qualities.