It is widely known that males and females typically possess different sound
characteristics when singing, such as timbre and pitch, but it has never been
explored whether these gender-based characteristics lead to a performance
disparity in singing voice transcription (SVT), whose target includes pitch.
Such a disparity could cause fairness issues and severely affect the user
experience of downstream SVT applications. Motivated by this, we first
demonstrate the female superiority of SVT systems, which is observed across
different models and datasets. We find that different pitch distributions,
rather than gender data imbalance, contribute to this disparity. To address
this issue, we propose using an attribute predictor to predict gender labels
and adversarially training the SVT system to enforce the gender-invariance of
acoustic representations. Leveraging the prior knowledge that pitch
distributions may contribute to the gender bias, we propose conditionally
aligning acoustic representations between demographic groups by feeding note
events to the attribute predictor. Empirical experiments on multiple benchmark
SVT datasets show that our method significantly reduces gender bias (up to more
than 50%) with negligible degradation of overall SVT performance, on both
in-domain and out-of-domain singing data, thus offering a better
fairness-utility trade-off.

通过使用属性预测器对韵律事件进行条件对齐，我们提出了一种减少性别偏见的方法，在不降低整体歌唱声音转录性能的情况下，显著减少了性别偏见，从而提供更好的公平性 - 实用性权衡。