  Speech enhancement attenuates interfering sounds in speech signals but may
introduce artifacts that perceivably deteriorate the output signal. We propose
a method for controlling the trade-off between the attenuation of the
interfering background signal and the loss of sound quality. A deep neural
network estimates the attenuation of the separated background signal such that
the sound quality, quantified using the Artifact-related Perceptual Score,
meets an adjustable target. Subjective evaluations indicate that consistent
sound quality is obtained across various input signals. Our experiments show
that the proposed method is able to control the trade-off with an accuracy that
is adequate for real-world dialogue enhancement applications.
