Neural Percussive Synthesis Parameterised by High-Level Timbral Features
This work addresses the need for user-friendly sound synthesis tools for musicians or audio engineers, but it is incremental as it builds on existing timbral features and neural network methods.
The authors tackled the problem of synthesizing percussive sounds with intuitive control over high-level timbral features using a deep neural network, resulting in a system that maps input parameters to waveforms and was evaluated with subjective listening tests.
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.