On the Generalization Ability of Complex-Valued Variational U-Networks for Single-Channel Speech Enhancement

The ability to generalize well to different environments is of importance for audio de-noising systems in real-world scenarios. Especially single-channel signals require efficient noise filtering without impacting speech intelligibility negatively. Our previous work has shown that a probabilistic latent space model combined with a U-Network architecture increases performance and generalization ability to some extent. Here, we further evaluate magnitude-only, as well as complex-valued U-Network models, on two different datasets, and in a train-test mismatch scenario. Adaptability of models is evaluated by introducing a curve-based score similar to area-under-the-curve metrics. The proposed probabilistic latent space models outperform their ablated variants in most conditions, as well as well-known comparison methods, while increases in network size are negligible. Improvements of up to 0.97 dB SI-SDR in matched, and 2.72 dB SI-SDR in mismatched conditions are observed, with highest total SI-SDR scores of 20.21 dB and 18.71 dB, respectively. The proposed stability-score aligns well with observed performance behaviour, further validating the probabilistic latent space model.