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Sound synthesis is a complex field that requires domain expertise. Manualtuning of synthesizer parameters to match a specific sound can be an exhaustivetask, even for experienced sound engineers. In this paper, we introduceInverSynth - an automatic method for synthesizer parameters tuning to match agiven input sound. InverSynth is based on strided convolutional neural networksand is capable of inferring the synthesizer parameters configuration from theinput spectrogram and even from the raw audio. The effectiveness InverSynth isdemonstrated on a subtractive synthesizer with four frequency modulatedoscillators, envelope generator and a gater effect. We present extensivequantitative and qualitative results that showcase the superiority InverSynthover several baselines. Furthermore, we show that the network depth is animportant factor that contributes to the prediction accuracy.

InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals