# Reverb Conversion of Mixed Vocal Tracks Using an End-to-end Convolutional Deep Neural Network

arXiv: https://arxiv.org/abs/2103.02147 · HTML: https://mlsanity.com/paper/2103.02147/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction](https://mlsanity.com/paper/1611.09842/) (2016)
- [Phase-aware Speech Enhancement with Deep Complex U-Net](https://mlsanity.com/paper/1903.03107/) (2019)
- [SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping](https://mlsanity.com/paper/2007.09131/) (2020)
- [Modeling plate and spring reverberation using a DSP-informed deep neural network](https://mlsanity.com/paper/1910.10105/) (2019)
- [SignalTrain: Profiling Audio Compressors with Deep Neural Networks](https://mlsanity.com/paper/1905.11928/) (2019)

## Built on by (most cited first, 8 of 8)

- [GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration](https://mlsanity.com/paper/2301.12686/) (2023)
- [Visual Acoustic Matching](https://mlsanity.com/paper/2202.06875/) (2022)
- [ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer](https://mlsanity.com/paper/2305.12708/) (2023)
- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [Unsupervised vocal dereverberation with diffusion-based generative models](https://mlsanity.com/paper/2211.04124/) (2022)
- [Automatic music mixing with deep learning and out-of-domain data](https://mlsanity.com/paper/2208.11428/) (2022)
- [End-to-end Music Remastering System Using Self-supervised and Adversarial Training](https://mlsanity.com/paper/2202.08520/) (2022)
- [EchoMark: Perceptual Acoustic Environment Transfer with Watermark-Embedded Room Impulse Response](https://mlsanity.com/paper/2511.06458/) (2025)

## Abstract (arXiv)

Reverb plays a critical role in music production, where it provides listeners with spatial realization, timbre, and texture of the music. Yet, it is challenging to reproduce the musical reverb of a reference music track even by skilled engineers. In response, we propose an end-to-end system capable of switching the musical reverb factor of two different mixed vocal tracks. This method enables us to apply the reverb of the reference track to the source track to which the effect is desired. Further, our model can perform de-reverberation when the reference track is used as a dry vocal source. The proposed model is trained in combination with an adversarial objective, which makes it possible to handle high-resolution audio samples. The perceptual evaluation confirmed that the proposed model can convert the reverb factor with the preferred rate of 64.8%. To the best of our knowledge, this is the first attempt to apply deep neural networks to converting music reverb of vocal tracks.