Elucidate Gender Fairness in Singing Voice Transcription
Xiangming Gu Wei Zeng Ye Wang
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.