# Effects of Convolutional Autoencoder Bottleneck Width on StarGAN-based Singing Technique Conversion

arXiv: https://arxiv.org/abs/2308.10021 · HTML: https://mlsanity.com/paper/2308.10021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks](https://mlsanity.com/paper/1806.02169/) (2018)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation](https://mlsanity.com/paper/2011.05731/) (2020)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network](https://mlsanity.com/paper/1912.01852/) (2019)
- [Unsupervised Singing Voice Conversion](https://mlsanity.com/paper/1904.06590/) (2019)
- [Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck](https://mlsanity.com/paper/1911.07460/) (2019)
- [Zero-shot Singing Technique Conversion](https://mlsanity.com/paper/2111.08839/) (2021)

## Abstract (arXiv)

Singing technique conversion (STC) refers to the task of converting from one voice technique to another while leaving the original singer identity, melody, and linguistic components intact. Previous STC studies, as well as singing voice conversion research in general, have utilized convolutional autoencoders (CAEs) for conversion, but how the bottleneck width of the CAE affects the synthesis quality has not been thoroughly evaluated. To this end, we constructed a GAN-based multi-domain STC system which took advantage of the WORLD vocoder representation and the CAE architecture. We varied the bottleneck width of the CAE, and evaluated the conversion results subjectively. The model was trained on a Mandarin dataset which features four singers and four singing techniques: the chest voice, the falsetto, the raspy voice, and the whistle voice. The results show that a wider bottleneck corresponds to better articulation clarity but does not necessarily lead to higher likeness to the target technique. Among the four techniques, we also found that the whistle voice is the easiest target for conversion, while the other three techniques as a source produce more convincing conversion results than the whistle.