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