  We present a deep neural network-based methodology for synthesising
percussive sounds with control over high-level timbral characteristics of the
sounds. This approach allows for intuitive control of a synthesizer, enabling
the user to shape sounds without extensive knowledge of signal processing. We
use a feedforward convolutional neural network-based architecture, which is
able to map input parameters to the corresponding waveform. We propose two
datasets to evaluate our approach on both a restrictive context, and in one
covering a broader spectrum of sounds. The timbral features used as parameters
are taken from recent literature in signal processing. We also use these
features for evaluation and validation of the presented model, to ensure that
changing the input parameters produces a congruent waveform with the desired
characteristics. Finally, we evaluate the quality of the output sound using a
subjective listening test. We provide sound examples and the system's source
code for reproducibility.
