This page includes of audio examples for the paper *Simionato R., Fasciani S., Fully Conditioned and Low-latency Black-box Modeling of Analog Compression, in Proceedings of the 26the Digital Audio Effects Conference (DAFx23), Copenhagen, Denmark, 2023*.

Associated code, dataset, and trained models are available on **GitHub**.

Examples are available for the following compressor models:

- CL 1B – Dataset – Encoder Decoder Model Examples
- CL 1B – Dataset – Encoder Decoder Light Model Examples
- LA-2A – Dataset – Encoder Decoder Model Examples
- LA-2A – Dataset – Encoder Decoder Light Model Examples
- PSP MicroComp – Dataset – Encoder Decoder Model examples
- U-he Presswerk – Dataset – Encoder Decoder Model examples
- Softube FET – Dataset – Encoder Decoder Model Examples
- CL 1B – Dataset – Temporal Convolutional Network Model Examples

Each example includes the following:

- Input Audio Signal
- Target Output Audio Signal (true output recorded from the device)
- Predicted Output Audio Signal (generated by the inference of the trained model)
- Waveform of Target and Predicted Output Audio Signal with respective RMS values
- Spectrograms of Target and Predicted Output Audio Signal

The title of the waveform plot details the compressor control parameters, valid for all devices except the LA-2A:

- at = attack time (ms)
- re = release time (s)
- ra = compression ratio (ra:1)
- th = threshold (dBu)

For the LA2A, the control parameters detailed in the title of the waveform plot are the following:

- M = mode (1 compression, 0 limit)
- p = peak reduction (dimensionless)

### CL 1B Dataset – Encoder Decoder Light Model

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### LA-2A Dataset – Encoder Decoder Light Model

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### CL 1B Dataset – Encoder Decoder Model

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### LA-2A Dataset – Encoder Decoder Model

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### PSP MicroComp Dataset – Encoder Decoder Model

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### U-he Presswerk Dataset – Encoder Decoder Model

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### Softube FET Dataset – Encoder Decoder Model

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### CL 1B Dataset – Temporal Convolutional Network Model

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