Novel Training Paradigm and Ablation Study on Integrating Background Noise with Variable Accuracy for Noise-Aware Speech Enhancement

Training noise-aware speech enhancement models is inherently tricky due to the implicit coupling with the estimator of noise characteristics. Thereby, it is difficult to simultaneously investigate robust noise representations to encode noise-related information while independently optimising the enhancement network architecture for incorporating noise-awareness. The systematic effect of different noise representations on a given noise-aware architecture or the effect of different noise-aware architectures for a given noise representation can, therefore, not be easily studied. To decouple the two aspects, we first propose a novel, tunable data-generation paradigm to generate reference noise signals with varying degrees of accuracy - reflecting estimation errors expected in practice. Next, we incorporate this framework to train a straightforward noise-aware speech enhancement model. Through a systematic ablation study, we show that the performance of the noise-aware framework steadily improves with the accuracy of the background noise information. Also, when the noise-reference is minimally informative, the noise-aware framework performs on par with the baseline (noiseblind) speech enhancement model. Applying this trained noiseaware framework to realistic and real-world captures, we further demonstrate the generalisability of the trained model to even completely unseen and mismatched scenarios - convincingly establishing the validity of the data-generation paradigm. This work, thereby, paves the way for systematic exploration of the benefit of noise-aware speech enhancement, permitting simultaneous investigation into network architecture and noiserepresentations, without imposing a limiting interconnection between these two aspects.