Controlling the Perceived Sound Quality for Dialogue Enhancement With Deep Learning

Abstract

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.

Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

“…In source separation, it is often of interest to assess the interferer reduction and the presence of distortions, artifacts, and colorations independently rather than jointly [55], [75]. This can give useful diagnostic information, e.g., for supporting the interpretation of a listening test [76] or for controlling the amount of interferer reduction such that a desired artifacts-related quality level is met [77]. As far as perceptual scores assessing exclusively artifacts, only one dataset is available (PEASS APS LT), in which listeners rated the quality in terms of absence of additional artificial noise.…”