This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address this, a method is proposed to enhance references and augment the mixtures with WHAM!, aiming to train models that avoid learning noisy references. Two models trained on these enhanced datasets are evaluated with the non-intrusive NISQA.v2 metric. Results show reduced noise in separated speech but suggest that processing references may introduce artefacts, limiting overall quality gains. Negative correlation is found between SI-SDR and perceived noisiness across models on the WSJ0-2Mix and Libri2Mix test sets, underlining the conclusion from the derivation.

翻译：本文探讨了在监督语音分离中，将尺度不变信失真比（SI-SDR）同时作为评估指标和训练目标时的影响，特别是当训练参考信号包含噪声的情况——这正是事实基准数据集WSJ0-2Mix的特征。对含噪参考条件下SI-SDR的推导表明，噪声会限制可达的SI-SDR值，或导致分离输出中出现不必要的噪声。为解决这一问题，本文提出了一种增强参考信号并用WHAM!数据集扩充混合信号的方法，旨在训练模型避免学习含噪参考。基于增强数据集训练的两个模型使用非侵入式NISQA.v2指标进行评估。结果显示，分离语音中的噪声有所减少，但处理参考信号可能引入伪影，限制了整体质量提升。在WSJ0-2Mix和Libri2Mix测试集上，各模型的SI-SDR与感知噪声度之间存在负相关，进一步印证了推导结论。