Intelligent multitrack dynamic range compression

Zheng Ma, Brecht De Man, Pedro D. L. Pestana, Dawn A. A. Black, Joshua D. Reiss

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


We present an intelligent approach to multitrack dynamic range compression where all parameters are configured automatically based on side-chain feature extraction from the input signals. A method of adjustment experiment to explore how audio engineers set the ratio and threshold is described. We use multiple linear regression to model the relationship between different features and the experimental results. Parameter automations incorporate control assumptions based on this experiment and those derived from mixing literature and analysis. Subjective evaluation of the intelligent system is provided in the form of a multiple stimulus listening test where the system is compared against a no-compression mix, two human mixes, and an alternative approach. Results showed that mixes devised by our system are able to compete with or outperform manual mixes by semi-professionals under a variety of subjective criteria.
Original languageEnglish
Pages (from-to)412-426
Number of pages15
JournalAES: Journal of the Audio Engineering Society
Issue number6
Publication statusPublished - 1 Jun 2015


Dive into the research topics of 'Intelligent multitrack dynamic range compression'. Together they form a unique fingerprint.

Cite this