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Controllable neural audio synthesis of sound effects is a challenging taskdue to the potential scarcity and spectro-temporal variance of the data.Differentiable digital signal processing (DDSP) synthesisers have beensuccessfully employed to model and control musical and harmonic signals usingrelatively limited data and computational resources. Here we proposeNoiseBandNet, an architecture capable of synthesising and controlling soundeffects by filtering white noise through a filterbank, thus going further thanprevious systems that make assumptions about the harmonic nature of sounds. Weevaluate our approach via a series of experiments, modelling footsteps,thunderstorm, pottery, knocking, and metal sound effects. ComparingNoiseBandNet audio reconstruction capabilities to four variants of theDDSP-filtered noise synthesiser, NoiseBandNet scores higher in nine out of tenevaluation categories, establishing a flexible DDSP method for generatingtime-varying, inharmonic sound effects of arbitrary length with both good timeand frequency resolution. Finally, we introduce some potential creative uses ofNoiseBandNet, by generating variations, performing loudness transfer, and bytraining it on user-defined control curves.

NoiseBandNet: Controllable Time-Varying Neural Synthesis of Sound Effects Using Filterbanks