2018/03/27 by David Ronan, Ronan, David, Zheng Ma +7
Computer Science · Neuroscience · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Hearing Loss and Rehabilitation #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1803.09960
openalex publication_date 2018/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The iterative process of masking minimisation when mixing multitrack audio is a challenging optimisation problem, in part due to the complexity and non-linearity of auditory perception. In this article, we first propose a multitrack masking metric inspired by the MPEG psychoacoustic model. We investigate different audio processing techniques to manipulate the frequency and dynamic characteristics of the signal in order to reduce masking based on the proposed metric. We also investigate whether or not automatically mixing using subgrouping is beneficial or not to perceived quality and clarity of a mix. Evaluation results suggest that our proposed masking metric when used in an automatic mixing framework can be used to reduce inter-channel auditory masking as well as improve the perceived quality and perceived clarity of a mix. Furthermore, our results suggest that using subgrouping in an automatic mixing framework can be used to improve the perceived quality and perceived clarity of a mix.