# Architecture and Affordances of PLAUD: Performative Latents and Unsupervised DDSP

arXiv: https://arxiv.org/abs/2608.13724 · HTML: https://mlsanity.com/paper/2608.13724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling](https://mlsanity.com/paper/2112.09312/) (2021)
- [Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations](https://mlsanity.com/paper/2412.08550/) (2024)
- [A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis](https://mlsanity.com/paper/2308.15422/) (2023)
- [I'm Sorry for Your Loss: Spectrally-Based Audio Distances Are Bad at Pitch](https://mlsanity.com/paper/2012.04572/) (2020)
- [Sinusoidal Frequency Estimation by Gradient Descent](https://mlsanity.com/paper/2210.14476/) (2022)
- [Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry](https://mlsanity.com/paper/2101.04035/) (2021)
- [Active Divergence with Generative Deep Learning -- A Survey and Taxonomy](https://mlsanity.com/paper/2107.05599/) (2021)
- [NoiseBandNet: Controllable Time-Varying Neural Synthesis of Sound Effects Using Filterbanks](https://mlsanity.com/paper/2307.08007/) (2023)
- [Sound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures](https://mlsanity.com/paper/2305.15571/) (2023)
- [Neutone SDK: An Open Source Framework for Neural Audio Processing](https://mlsanity.com/paper/2508.09126/) (2025)
- [Exploring Situated Stabilities of a Rhythm Generation System through Variational Cross-Examination](https://mlsanity.com/paper/2509.05145/) (2025)

## Abstract (arXiv)

PLAUD (Performative Latents and Unsupervised DDSP) is a neural synthesizer and Max for Live instrument for live electronic music, built on NoiseBandNet and trained on small personal sound corpora. We present its architecture, combining a variational DDSP synthesis model, latent smoothing, multi-scale spectral and adversarial losses, and an optional transformer prior, alongside a set of bending operations that intervene directly in the synthesis chain: component limiting, waveshaping, and prior feedback. The Max for Live interface exposes control generation, trajectory sampling, and modulation as primary modes of interaction. Throughout, we thread an affordance analysis arguing that the system's performative character follows from architectural decisions rather than being designed on top of them. The paper contributes both a technical account of the system and a situated affordance analysis of its role in live electronic music performance.