The reduction of auditory masking is a crucial objective when mixing multitrack audio and is typically achieved through manipulation of gain, equalization, and/or panning for each stem in a mix. However, some amount of masking is unavoidable, acceptable, or even desirable in certain situations. Current automatic mixing approaches often focus on the reduction of masking in general, rather than focusing on particularly problematic masking. As a first step in focusing the attention of automatic masking reduction algorithms on problematic rather than known and accepted masking, we use psychoacoustic masking models to analyze multitrack mixes produced by experienced audio engineers. We measure masking in terms of loudness loss and present problematic masking as outliers (values above the 95th percentile) in instrument and frequency-dependent distributions.
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