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Dialogue Enhancement (DE) enables the rebalancing of dialogue and backgroundsounds to fit personal preferences and needs in the context of broadcast audio.When individual audio stems are unavailable from production, DialogueSeparation (DS) can be applied to the final audio mixture to obtain estimatesof these stems. This work focuses on Preferred Loudness Differences (PLDs)between dialogue and background sounds. While previous studies determined thePLD through a listening test employing original stems from production, stemsestimated by DS are used in the present study. In addition, a larger variety ofsignal classes is considered. PLDs vary substantially across individuals(average interquartile range: 5.7 LU). Despite this variability, PLDs are foundto be highly dependent on the signal type under consideration, and it is shownthat median PLDs can be predicted using objective intelligibility metrics. Twoexisting baseline prediction methods - intended for use with original stems -displayed a Mean Absolute Error (MAE) of 7.5 LU and 5 LU, respectively. Amodified baseline (MAE: 3.2 LU) and an alternative approach (MAE: 2.5 LU) areproposed. Results support the viability of processing final broadcast mixtureswith DS and offering an alternative remixing that accounts for median PLDs.

Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio