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Z. Ma, B. De Man, PE. L.. Pestana, DA. A.. Black, and JO. D.. Reiss, "Intelligent Multitrack Dynamic Range Compression," J. Audio Eng. Soc., vol. 63, no. 6, pp. 412-426, (2015 June.). doi: https://doi.org/10.17743/jaes.2015.0053 Z. Ma, B. De Man, PE. L.. Pestana, DA. A.. Black, and JO. D.. Reiss, "Intelligent Multitrack Dynamic Range Compression," J. Audio Eng. Soc., vol. 63 Issue 6 pp. 412-426, (2015 June.). doi: https://doi.org/10.17743/jaes.2015.0053
Abstract: An intelligent dynamic range compression (DRC) algorithm, using the CA-DAFX processing architecture, produces the optimal amount of dynamic range for multitrack recordings. The algorithm exploits the interdependence of input audio features, incorporates best practices, and uses subjective evaluation. The classical parameters of a typical compressor (ratio, threshold, knee, attack, and release) are dynamically adjusted depending on extracted features and control rules. Two new audio weighting features, percussiveness and low-frequency strength, were proposed to incorporate the transient nature and spectral content of the signal. The authors applied multiple linear regression models to the subjective results to formulate the ratio and threshold automations that follow the choices of the human operators. The results showed that the algorithm can compete with or outperform semiprofessional mixes in terms of four different perceptual criteria: the appropriateness of the amount of DRC applied, the degree of imperfection, the ability to stabilize the erratic level fluctuations, and overall Preferences.

@article{ma2015intelligent,
author={ma, zheng and de man, brecht and pestana, pedro d. l. and black, dawn a. a. and reiss, joshua d.},
journal={journal of the audio engineering society},
title={intelligent multitrack dynamic range compression},
year={2015},
volume={63},
number={6},
pages={412-426},
doi={https://doi.org/10.17743/jaes.2015.0053},
month={june},} @article{ma2015intelligent,
author={ma, zheng and de man, brecht and pestana, pedro d. l. and black, dawn a. a. and reiss, joshua d.},
journal={journal of the audio engineering society},
title={intelligent multitrack dynamic range compression},
year={2015},
volume={63},
number={6},
pages={412-426},
doi={https://doi.org/10.17743/jaes.2015.0053},
month={june},
abstract={an intelligent dynamic range compression (drc) algorithm, using the ca-dafx processing architecture, produces the optimal amount of dynamic range for multitrack recordings. the algorithm exploits the interdependence of input audio features, incorporates best practices, and uses subjective evaluation. the classical parameters of a typical compressor (ratio, threshold, knee, attack, and release) are dynamically adjusted depending on extracted features and control rules. two new audio weighting features, percussiveness and low-frequency strength, were proposed to incorporate the transient nature and spectral content of the signal. the authors applied multiple linear regression models to the subjective results to formulate the ratio and threshold automations that follow the choices of the human operators. the results showed that the algorithm can compete with or outperform semiprofessional mixes in terms of four different perceptual criteria: the appropriateness of the amount of drc applied, the degree of imperfection, the ability to stabilize the erratic level fluctuations, and overall preferences.},}

TY - paper
TI - Intelligent Multitrack Dynamic Range Compression
SP - 412 EP - 426
AU - Ma, Zheng
AU - De Man, Brecht
AU - Pestana, Pedro D. L.
AU - Black, Dawn A. A.
AU - Reiss, Joshua D.
PY - 2015
JO - Journal of the Audio Engineering Society
IS - 6
VO - 63
VL - 63
Y1 - June 2015 TY - paper
TI - Intelligent Multitrack Dynamic Range Compression
SP - 412 EP - 426
AU - Ma, Zheng
AU - De Man, Brecht
AU - Pestana, Pedro D. L.
AU - Black, Dawn A. A.
AU - Reiss, Joshua D.
PY - 2015
JO - Journal of the Audio Engineering Society
IS - 6
VO - 63
VL - 63
Y1 - June 2015
AB - An intelligent dynamic range compression (DRC) algorithm, using the CA-DAFX processing architecture, produces the optimal amount of dynamic range for multitrack recordings. The algorithm exploits the interdependence of input audio features, incorporates best practices, and uses subjective evaluation. The classical parameters of a typical compressor (ratio, threshold, knee, attack, and release) are dynamically adjusted depending on extracted features and control rules. Two new audio weighting features, percussiveness and low-frequency strength, were proposed to incorporate the transient nature and spectral content of the signal. The authors applied multiple linear regression models to the subjective results to formulate the ratio and threshold automations that follow the choices of the human operators. The results showed that the algorithm can compete with or outperform semiprofessional mixes in terms of four different perceptual criteria: the appropriateness of the amount of DRC applied, the degree of imperfection, the ability to stabilize the erratic level fluctuations, and overall Preferences.