Controlling the Perceived Sound Quality for Dialogue Enhancement With Deep Learning

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 n...

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Bibliographic Details
Published inICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 51 - 55
Main Authors Uhle, Christian, Torcoli, Matteo, Paulus, Jouni
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2020
Subjects
Artifact-related Perceptual Score
Attenuation
Deep Learning
Dialogue Enhancement
Neural networks
Parameter estimation
Signal processing
Speech enhancement
System performance
Training
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9053789