  Sound synthesis is a complex field that requires domain expertise. Manual
tuning of synthesizer parameters to match a specific sound can be an exhaustive
task, even for experienced sound engineers. In this paper, we introduce
InverSynth - an automatic method for synthesizer parameters tuning to match a
given input sound. InverSynth is based on strided convolutional neural networks
and is capable of inferring the synthesizer parameters configuration from the
input spectrogram and even from the raw audio. The effectiveness InverSynth is
demonstrated on a subtractive synthesizer with four frequency modulated
oscillators, envelope generator and a gater effect. We present extensive
quantitative and qualitative results that showcase the superiority InverSynth
over several baselines. Furthermore, we show that the network depth is an
important factor that contributes to the prediction accuracy.
