Music producers and casual users often seek to replicate dynamic range compression used in a particular recording or production context for their own track. However, not knowing the parameter settings used to produce the audio using the effect may become an impediment, especially for beginners or untrained users who may lack critical listening skills. We address this issue by presenting an automatic compressor plugin relying on a neural network to extract relevant features from a reference signal and estimate compression parameters. The plugin automatically adjusts its parameters to match the input signal with a reference audio recording as closely as possible. Quantitative and qualitative usability evaluation of the plugin was conducted with amateur, pro-amateur and professional music producers. The results established acceptance of the core idea behind the proposed control method across these user groups.
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can
subscribe to this RSS feed.
Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.
