  Systems for synthesizer sound matching, which automatically set the
parameters of a synthesizer to emulate an input sound, have the potential to
make the process of synthesizer programming faster and easier for novice and
experienced musicians alike, whilst also affording new means of interaction
with synthesizers. Considering the enormous variety of synthesizers in the
marketplace, and the complexity of many of them, general-purpose sound matching
systems that function with minimal knowledge or prior assumptions about the
underlying synthesis architecture are particularly desirable. With this in
mind, we introduce a synthesizer sound matching model based on the Audio
Spectrogram Transformer. We demonstrate the viability of this model by training
on a large synthetic dataset of randomly generated samples from the popular
Massive synthesizer. We show that this model can reconstruct parameters of
samples generated from a set of 16 parameters, highlighting its improved
fidelity relative to multi-layer perceptron and convolutional neural network
baselines. We also provide audio examples demonstrating the out-of-domain model
performance in emulating vocal imitations, and sounds from other synthesizers
and musical instruments.
