4(Feed-Forward)A-2A: an emulator plugin for LA-2A optical compressor

4A-2A is a VST plugin that emulates the LA-2A optical compressor using a feed-forward digital compressor. It was first presented as Emulating LA-2A Optical Compressor With a Feed-Forward Digital Compressor Using the Newton-Raphson Method at DMRN+19. The finished work, Sound Matching an Analogue Levelling Amplifier Using the Newton-Raphson Method, was accepted at the AES International Conference on Artificial Intelligence and Machine Learning for Audio in London, UK.

We implement a mapping function $\mathbb{R} \to \mathbb{R}^5$ that maps the peak reduction of the LA-2A to the five parameters of the feed-forward compressor. The mapping function is learnt using the Newton-Raphson method on the SignalTrain dataset.

Interface

The plugin comes with six sliders and one button, which are:

1. Threshold - The threshold of the compressor in dB.
2. Ratio - The ratio of the compressor (1 to 20).
3. Attack - The attack time of the compressor in ms (0.1 to 100).
4. Release - The release time of the compressor in ms (100 to 1000).
5. Make-up - The make-up gain of the compressor in dB.
6. Peak Reduction - The emulated peak reduction of the LA-2A (40 to 100). This slider controls the other five sliders and overrides their values.
7. Comp./Limit. - The mode of the compressor. When the button is changed, sliders 1-5 are also changed based on the peak reduction.

Installation

The pre-built VST3 binaries for Windows and macOS are available in the releases section. One can also build the plugin from source using the Projucer project file.

Training code

The source code of the papers is implemented in Python and is listed in the python directory. For details, please refer to the README file in the directory.

Citation

If you use this plugin in your research, please consider citing the following papers:

@inproceedings{ycy2024emulating,
  title={Emulating LA-2A Optical Compressor With a Feed-Forward Digital Compressor Using the Newton-Raphson Method},
  author={Chin-Yun Yu and György Fazekas},
  booktitle={Proceedings of the Digital Music Research Network Workshop},
  year={2024}
}

@inproceedings{ycy2025newton,
  title={Sound Matching an Analogue Levelling Amplifier Using the Newton-Raphson Method},
  author={Chin-Yun Yu and György Fazekas},
  booktitle={AES International Conference on Artificial Intelligence and Machine Learning for Audio},
  address={London, UK},
  year={2025},
}