In this work, the problem of the audibility of background music in television programs is studied. After reviewing the state of the art and verifying its incipient state, the problem is faced considering the 3 levels of audibility defined by the WIPO and beginning the study trying to find the threshold between inaudible and audible. It is considered that the music and voice tracks are available separately and a series of subjective tests are prepared, carried out with abundant realistic material and in controlled conditions as similar as possible to the television room of an average home. An analysis of the results reveals that the difference in integrated loudness between voice and music is the most defining factor in audibility, although the type of music also reveals a certain influence. To take this influence into account, various indicators related to the momentary loudness of the signal were tested, finally obtaining a highly correlated statistic. By means of a linear regression, an expression dependent on both parameters was obtained that provides a very stable final estimator and with a mean error with respect to the jury's mean of about 0.9 dB for the sound material tested. This result can serve as a basis for the elaboration of a recommendation in this field. For the case of broadcast analysis where voice and music are mixed, the new voice-music separation techniques based on deep learning neural networks allow resynthesizing both isolated tracks at the destination to apply the proposed algorithm.
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