# Neural-Enhanced Dynamic Range Compression Inversion: A Hybrid Approach for Restoring Audio Dynamics

arXiv: https://arxiv.org/abs/2411.04337 · HTML: https://mlsanity.com/paper/2411.04337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1706.07156/) (2017)
- [General Purpose Audio Effect Removal](https://mlsanity.com/paper/2308.16177/) (2023)
- [End-to-end Music Remastering System Using Self-supervised and Adversarial Training](https://mlsanity.com/paper/2202.08520/) (2022)
- [Blind estimation of audio effects using an auto-encoder approach and differentiable digital signal processing](https://mlsanity.com/paper/2310.11781/) (2023)
- [Music De-limiter Networks via Sample-wise Gain Inversion](https://mlsanity.com/paper/2308.01187/) (2023)

## Abstract (arXiv)

Dynamic Range Compression (DRC) is a widely used audio effect that adjusts signal dynamics for applications in music production, broadcasting, and speech processing. Inverting DRC is of broad importance for restoring the original dynamics, enabling remixing, and enhancing the overall audio quality. Existing DRC inversion methods either overlook key parameters or rely on precise parameter values, which can be challenging to estimate accurately. To address this limitation, we introduce a hybrid approach that combines model-based DRC inversion with neural networks to achieve robust DRC parameter estimation and audio restoration simultaneously. Our method uses tailored neural network architectures (classification and regression), which are then integrated into a model-based inversion framework to reconstruct the original signal. Experimental evaluations on various music and speech datasets confirm the effectiveness and robustness of our approach, outperforming several state-of-the-art techniques.