The process of audio mastering often, if not always, includes various audio signal processing techniques such as frequency equalization and dynamic range compression. With respect to the genre and style of the audio content, the parameters of these techniques are controlled by a mastering engineer, in order to process the original audio material. This operation relies on musical and perceptually pleasing facets of the perceived acoustic characteristics, transmitted from the audio material under the mastering process. Modeling such dynamic operations, which involve adaptation regarding the audio content, becomes vital in automated applications since it significantly affects the overall performance. In this work we present a system capable of modelling such behavior focusing on the automatic dynamic range compression. It predicts frequency coefficients that allow the dynamic range compression, via a trained deep neural network, and applies them to unmastered audio signal served as input. Both dynamic range compression and the prediction of the corresponding frequency coefficients take place inside the time-frequency domain, using magnitude spectra acquired from a critical band filter bank, similar to humans’ peripheral auditory system. Results from conducted listening tests, incorporating professional music producers and audio mastering engineers, demonstrate on average an equivalent performance compared to professionally mastered audio content. Improvements were also observed when compared to relevant and commercial software.
Mimilakis, Stylianos Ioannis; Drossos, Konstantinos; Virtanen, Tuomas; Schuller, Gerald
Affiliations: Fraunhofer Institute for Digital Media Technology (IDMT), Ilmenau, Germany; Tampere University of Technology, Tampere, Finland(See document for exact affiliation information.)
AES Convention:140 (May 2016) Paper Number:9539
Publication Date:May 26, 2016
Subject:Audio Content Management & Applications in Audio
No AES members have commented on this paper yet.
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.