  Controllable neural audio synthesis of sound effects is a challenging task
due to the potential scarcity and spectro-temporal variance of the data.
Differentiable digital signal processing (DDSP) synthesisers have been
successfully employed to model and control musical and harmonic signals using
relatively limited data and computational resources. Here we propose
NoiseBandNet, an architecture capable of synthesising and controlling sound
effects by filtering white noise through a filterbank, thus going further than
previous systems that make assumptions about the harmonic nature of sounds. We
evaluate our approach via a series of experiments, modelling footsteps,
thunderstorm, pottery, knocking, and metal sound effects. Comparing
NoiseBandNet audio reconstruction capabilities to four variants of the
DDSP-filtered noise synthesiser, NoiseBandNet scores higher in nine out of ten
evaluation categories, establishing a flexible DDSP method for generating
time-varying, inharmonic sound effects of arbitrary length with both good time
and frequency resolution. Finally, we introduce some potential creative uses of
NoiseBandNet, by generating variations, performing loudness transfer, and by
training it on user-defined control curves.
