# CycleFlow: Purify Information Factors by Cycle Loss

arXiv: https://arxiv.org/abs/2110.09928 · HTML: https://mlsanity.com/paper/2110.09928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 59 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data](https://mlsanity.com/paper/1709.07902/) (2017)
- [Hierarchical Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1810.07217/) (2018)
- [Unsupervised Speech Decomposition via Triple Information Bottleneck](https://mlsanity.com/paper/2004.11284/) (2020)
- [One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization](https://mlsanity.com/paper/1904.05742/) (2019)
- [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks](https://mlsanity.com/paper/1806.02169/) (2018)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder](https://mlsanity.com/paper/1610.04019/) (2016)
- [Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/1704.00849/) (2017)
- [VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture](https://mlsanity.com/paper/2006.04154/) (2020)
- [Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning](https://mlsanity.com/paper/2103.09420/) (2021)
- [Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1711.11293/) (2017)
- [Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations](https://mlsanity.com/paper/1804.02812/) (2018)
- [Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations](https://mlsanity.com/paper/2010.12231/) (2020)
- [S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations](https://mlsanity.com/paper/2104.02901/) (2021)
- [Non-Parallel Voice Conversion with Cyclic Variational Autoencoder](https://mlsanity.com/paper/1907.10185/) (2019)
- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities](https://mlsanity.com/paper/1909.10302/) (2019)
- [ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder](https://mlsanity.com/paper/1808.05092/) (2018)
- [Many-to-Many Voice Conversion using Conditional Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2002.06328/) (2020)
- [Global Rhythm Style Transfer Without Text Transcriptions](https://mlsanity.com/paper/2106.08519/) (2021)
- [Deep Factorization for Speech Signal](https://mlsanity.com/paper/1706.01777/) (2017)

## Abstract (arXiv)

SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, however, is that if the IB channels are not well designed, the resultant factors cannot be well disentangled. In this study, we propose a CycleFlow model that combines random factor substitution and cycle loss to solve this problem. Experiments on voice conversion tasks demonstrate that this simple technique can effectively reduce mutual information among individual factors, and produce clearly better conversion than the IB-based SpeechFlow. CycleFlow can also be used as a powerful tool for speech editing. We demonstrate this usage by an emotion perception experiment.