# Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects

arXiv: https://arxiv.org/abs/2211.02247 · HTML: https://mlsanity.com/paper/2211.02247/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Understanding Dimensional Collapse in Contrastive Self-supervised Learning](https://mlsanity.com/paper/2110.09348/) (2021)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Measuring Disentanglement: A Review of Metrics](https://mlsanity.com/paper/2012.09276/) (2020)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [Style Transfer of Audio Effects with Differentiable Signal Processing](https://mlsanity.com/paper/2207.08759/) (2022)
- [Efficient neural networks for real-time modeling of analog dynamic range compression](https://mlsanity.com/paper/2102.06200/) (2021)
- [Differentiable Artificial Reverberation](https://mlsanity.com/paper/2105.13940/) (2021)
- [Automatic music mixing with deep learning and out-of-domain data](https://mlsanity.com/paper/2208.11428/) (2022)
- [Differentiable Signal Processing With Black-Box Audio Effects](https://mlsanity.com/paper/2105.04752/) (2021)
- [Reverb Conversion of Mixed Vocal Tracks Using an End-to-end Convolutional Deep Neural Network](https://mlsanity.com/paper/2103.02147/) (2021)
- [End-to-end Music Remastering System Using Self-supervised and Adversarial Training](https://mlsanity.com/paper/2202.08520/) (2022)

## Built on by (most cited first, 18 of 18)

- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [TokenSynth: A Token-based Neural Synthesizer for Instrument Cloning and Text-to-Instrument](https://mlsanity.com/paper/2502.08939/) (2025)
- [Learning to Highlight Audio by Watching Movies](https://mlsanity.com/paper/2505.12154/) (2025)
- [Adoption of AI Technology in the Music Mixing Workflow: An Investigation](https://mlsanity.com/paper/2304.03407/) (2023)
- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data](https://mlsanity.com/paper/2307.12576/) (2023)
- [The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineers](https://mlsanity.com/paper/2309.03404/) (2023)
- [Segment Transformer: AI-Generated Music Detection via Music Structural Analysis](https://mlsanity.com/paper/2509.08283/) (2025)
- [Fusion Segment Transformer: Bi-Directional Attention Guided Fusion Network for AI-Generated Music Detection](https://mlsanity.com/paper/2601.13647/) (2026)
- [Conditional Flow Matching for Visually-Guided Acoustic Highlighting](https://mlsanity.com/paper/2602.03762/) (2026)
- [Operational Latent Spaces](https://mlsanity.com/paper/2406.02699/) (2024)
- [Advancing Music Therapy: Integrating Eastern Five-Element Music Theory and Western Techniques with AI in the Novel Five-Element Harmony System](https://mlsanity.com/paper/2412.06600/) (2024)
- [Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior](https://mlsanity.com/paper/2505.11315/) (2025)
- [WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling](https://mlsanity.com/paper/2507.10534/) (2025)
- [It`s All About Speed: AI`s Impact on Workflow in Music Production](https://mlsanity.com/paper/2605.29931/) (2026)
- [HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark](https://mlsanity.com/paper/2606.01686/) (2026)
- [Diff2Mix: Controllable Music Mixing via Diffusion Models and Differentiable Audio Effects](https://mlsanity.com/paper/2608.05442/) (2026)

## Abstract (arXiv)

We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from an already-processed wet multitrack dataset with an effective data preprocessing method that alleviates the data scarcity of obtaining unprocessed dry data. We analyze the proposed encoder for the disentanglement capability of audio effects and also validate its performance for mixing style transfer through both objective and subjective evaluations. From the results, we show the proposed system not only converts the mixing style of multitrack audio close to a reference but is also robust with mixture-wise style transfer upon using a music source separation model.