A Data-driven Distance Metric for Evaluating the effects of Dynamic Range Compression in Adverse Conditions.

Dynamic range compression is a crucial component in hearing aids, aiming to restore audibility for hearing-impaired listeners. However, determining suitable compression parameters, such as the time constants for the level estimation stage, remains a topic of debate, as the perceptual benefit of different parameter configurations varies depending on the acoustic conditions. In this study, a data-driven distance metric based on physical metrics was developed to evaluate and compare the performance of various compression systems. This analysis encompassed fast-acting and slow-acting compression, as well as a "scene-aware" compression that dynamically adjusted the release time constant based on the presence of the target. A reference system called "source-independent compression" was also considered, which had access to individual speech and noise signals. Multiple physical metrics were employed to assess the effects of these different compression systems under diverse acoustic conditions, including varying levels of interfering noise and degrees of room reverberation. Factor analysis was applied to derive a concise set of interpretable features representing the impact of compression, expressed as linear combinations of carefully selected objective metrics. The reduced dimensional representation enabled the use of the Manhattan distance to measure the similarity between the compression systems. Results demonstrated that the scene-aware compressor generally exhibited a smaller distance to the reference system compared to both the fast-acting and slow-acting compressors. This finding suggests that an adaptive compression system holds potential benefits across a range of acoustic conditions.