# Towards Controllable Audio Texture Morphing

arXiv: https://arxiv.org/abs/2304.11648 · HTML: https://mlsanity.com/paper/2304.11648/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [Rethinking CNN Models for Audio Classification](https://mlsanity.com/paper/2007.11154/) (2020)
- [Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1906.08152/) (2019)
- [Comparing Representations for Audio Synthesis Using Generative Adversarial Networks](https://mlsanity.com/paper/2006.09266/) (2020)
- [A Proposal for Foley Sound Synthesis Challenge](https://mlsanity.com/paper/2207.10760/) (2022)
- [A Non-iterative Method for (Re)Construction of Phase from STFT Magnitude](https://mlsanity.com/paper/1609.00291/) (2016)
- [Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks](https://mlsanity.com/paper/2103.07390/) (2021)
- [DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs](https://mlsanity.com/paper/2108.01216/) (2021)
- [Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures](https://mlsanity.com/paper/2208.10743/) (2022)
- [Sound texture synthesis using RI spectrograms](https://mlsanity.com/paper/1910.09497/) (2019)

## Built on by (most cited first, 4 of 4)

- [SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model](https://mlsanity.com/paper/2410.02144/) (2024)
- [Example-Based Framework for Perceptually Guided Audio Texture Generation](https://mlsanity.com/paper/2308.11859/) (2023)
- [Mix2Morph: Learning Sound Morphing from Noisy Mixes](https://mlsanity.com/paper/2601.20426/) (2026)
- [VoxMorph: Scalable Zero-shot Voice Identity Morphing via Disentangled Embeddings](https://mlsanity.com/paper/2601.20883/) (2026)

## Abstract (arXiv)

In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provides smooth morphing between the generated audio textures, and shows similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis.