  Listening to the audio of TV broadcast signals can be challenging for
hearing-impaired as well as normal-hearing listeners, especially when
background sounds are prominent or too loud compared to the speech signal. This
can result in a reduced satisfaction and increased listening effort of the
listeners. Since the broadcast sound is usually premixed, we perform a
subjective evaluation for quantifying the potential of speech enhancement
systems based on audio source separation and recurrent neural networks (RNN).
Recently, RNNs have shown promising results in the context of sound source
separation and real-time signal processing. In this paper, we separate the
speech from the background signals and remix the separated sounds at a higher
signal-to-noise ratio. This differs from classic speech enhancement, where
usually only the extracted speech signal is exploited. The subjective
evaluation with 20 normal-hearing subjects on real TV-broadcast material shows
that our proposed enhancement system is able to reduce the listening effort by
around 2 points on a 13-point listening effort rating scale and increases the
perceived sound quality compared to the original mixture.
