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Controlling the Perceived Sound Quality for Dialogue Enhancement with Deep Learning

: Uhle, C.; Torcoli, M.; Paulus, J.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings : May 4-8, 2020, Barcelona, Spain
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-5090-6631-5
ISBN: 978-1-5090-6632-2
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <45, 2020, Barcelona>
Fraunhofer IIS ()

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 tradeoff with an accuracy that is adequate for real-world dialogue enhancement applications.