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Intelligent dynamic range compression

Dimitrios Giannoulis, Michael Massberg, Zheng Ma, Jacob Maddams, Saoirse Finn, Joshua D. Reiss

This project investigates the design of high performance dynamic range compressors with the parameter settings automatically configured based on the signal content.

Dynamic range compression, despite being one of the most widely used audio effects, is still poorly understood and there is little formal knowledge and analysis of compressor design techniques. In this tutorial we describe several different approaches to digital dynamic range compressor design. Digital implementations of several classic analog approaches are given, as well as designs from recent literature, and new approaches that address possible issues. Several design techniques are analysed and compared, including RMS and peak – based approaches, feedforward and feedback designs, and linear and log domain level detection. We explain what makes the designs sound different, and provide distortion-based metrics to analyse their quality. We provide recommendations for high performance compressor design.

We minimised the number of user-adjustable controls by developing methods to automatically set the different compressor parameters at run-time and dependent on input signal statistics. The resulting automatic compressor can be operated with only one control and is implemented as a real-time audio plug-in. Finally we evaluate the automatic compressor settings against those made by expert human operators.

We then designed a system for automatically setting the parameters of multiple dynamic range compressors (one acting on each track of the multi-track mix) is described. The perceptual signal features loudness and loudness range are used to cross-adaptively control each compressor.
The system is fully autonomous and includes six different modes of operation. These were compared and evaluated against a mix in which compressor settings were chosen by an expert audio mix engineer. Clear preferences were established for the different modes of operation, and it was found that the autonomous system was capable of producing audio mixes of approximately
the same subjective quality as those produced by the expert engineer.

Source code

matlab source code for tutorial

autocompressor VST plugin and source code

Audio samples

to appear