# DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech

arXiv: https://arxiv.org/abs/2401.08102 · HTML: https://mlsanity.com/paper/2401.08102/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

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

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation](https://mlsanity.com/paper/2109.05418/) (2021)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration](https://mlsanity.com/paper/2204.05841/) (2022)
- [Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis](https://mlsanity.com/paper/2103.14201/) (2021)
- [Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech](https://mlsanity.com/paper/2107.07503/) (2021)
- [Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems](https://mlsanity.com/paper/2003.12425/) (2020)
- [DDS: A new device-degraded speech dataset for speech enhancement](https://mlsanity.com/paper/2109.07931/) (2021)
- [Neural Vocoder Feature Estimation for Dry Singing Voice Separation](https://mlsanity.com/paper/2211.15948/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multi-Source Spatial Knowledge Understanding for Immersive Visual Text-to-Speech](https://mlsanity.com/paper/2410.14101/) (2024)
- [U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model](https://mlsanity.com/paper/2507.14237/) (2025)
- [VividVoice: A Unified Framework for Scene-Aware Visually-Driven Speech Synthesis](https://mlsanity.com/paper/2602.02591/) (2026)

## Abstract (arXiv)

Properly setting up recording conditions, including microphone type and placement, room acoustics, and ambient noise, is essential to obtaining the desired acoustic characteristics of speech. In this paper, we propose Diff-R-EN-T, a Diffusion model for Recording ENvironment Transfer which transforms the input speech to have the recording conditions of a reference speech while preserving the speech content. Our model comprises the content enhancer, the recording environment encoder, and the diffusion decoder which generates the target mel-spectrogram by utilizing both enhancer and encoder as input conditions. We evaluate DiffRENT in the speech enhancement and acoustic matching scenarios. The results show that DiffRENT generalizes well to unseen environments and new speakers. Also, the proposed model achieves superior performances in objective and subjective evaluation. Sound examples of our proposed model are available online.