Profiling Audio Compressors with Deep Neural Networks

Hawley S, Colburn B, Mimilakis SI. Profiling Audio Compressors with Deep Neural Networks. In: AES Convention 147. Audio Engineering Society; 2019. Paper 10222. Available from: https://aes.org/publications/elibrary-page/?id=20595

@inproceedings{Hawley2019_20595,
  author = {Hawley, Scott and Colburn, Benjamin and Mimilakis, Stylianos Ioannis},
  title = {{Profiling Audio Compressors with Deep Neural Networks}},
  booktitle = {AES Convention 147},
  note = {Paper 10222},
  year = {2019},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20595}
}

TY  - CPAPER
TI  - Profiling Audio Compressors with Deep Neural Networks
AU  - Hawley, Scott
AU  - Colburn, Benjamin
AU  - Mimilakis, Stylianos Ioannis
T2  - AES Convention 147
M1  - Paper 10222
PY  - 2019
DA  - 2019/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20595
PB  - Audio Engineering Society
LA  - en
AB  - We present a data-driven approach for predicting the behavior of (i.e., profiling) a given parameterized, non-linear time-dependent audio signal processing effect. Our objective is to learn a mapping function that maps the unprocessed audio to the processed, using time-domain samples. We employ a deep auto-encoder model that is conditioned on both time-domain samples and the control parameters of the target audio effect. As a test-case, we focus on the offline profiling of two dynamic range compressors, one software-based and the other analog. Our results show that the primary characteristics of the compressors can be captured, however there is still sufficient audible noise to merit further investigation before such methods are applied to real-world audio processing workflows.
ER  -