In multitrack music production, some sounds get masked by other sounds and the listener has less ability to fully hear and distinguish the sound sources in the mix. The authors designed a simplified measure of masking based on best practices, and then implemented both an off-line and real-time, autonomous multitrack equalization system that reduces masking in multitrack audio. The system used objective measures of spectral masking in the resultant mixes. Listening tests provided a subjective comparison between the mix results of different implementations of the system, a raw mix, and manual mixes made by an amateur and a professional mix engineer. The results showed that autonomous systems reduce both the perceived and objective masking. The offline semi-autonomous system is capable of improving the raw mix better than an amateur and close to a professional mix by simply controlling one user parameter. The results also suggest that existing objective measures of masking are ill-suited for quantifying perceived masking in multitrack musical audio.
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