# Neural Percussive Synthesis Parameterised by High-Level Timbral Features

arXiv: https://arxiv.org/abs/1911.11853 · HTML: https://mlsanity.com/paper/1911.11853/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [One Billion Audio Sounds from GPU-enabled Modular Synthesis](https://mlsanity.com/paper/2104.12922/) (2021)
- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks](https://mlsanity.com/paper/2110.09605/) (2021)
- [Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds](https://mlsanity.com/paper/2202.00200/) (2022)
- [LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters](https://mlsanity.com/paper/2105.10371/) (2021)
- [The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis](https://mlsanity.com/paper/2505.03337/) (2025)
- [IS3 : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering](https://mlsanity.com/paper/2509.02622/) (2025)
- [Pitch-Conditioned Instrument Sound Synthesis From an Interactive Timbre Latent Space](https://mlsanity.com/paper/2510.04339/) (2025)

## Abstract (arXiv)

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.