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。

揭示歌声转录中的性别公平

Elucidate Gender Fairness in Singing Voice Transcription

Most fair machine learning methods either highly rely on the sensitive
information of the training samples or require a large modification on the
target models, which hinders their practical application. To address this
issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the
loss over the reweighted data set (second stage) where the sample weights are
computed to balance the model performance across different demographic groups
(first stage). FAIRIF can be applied on a wide range of models trained by
stochastic gradient descent without changing the model, while only requiring
group annotations on a small validation set to compute sample weights.
Theoretically, we show that, in the classification setting, three notions of
disparity among different groups can be mitigated by training with the weights.
Experiments on synthetic data sets demonstrate that FAIRIF yields models with
better fairness-utility trade-offs against various types of bias; and on
real-world data sets, we show the effectiveness and scalability of FAIRIF.
Moreover, as evidenced by the experiments with pretrained models, FAIRIF is
able to alleviate the unfairness issue of pretrained models without hurting
their performance.

我们提出了一个名为 FAIRIF 的两阶段训练算法，它可以应用于一系列使用随机梯度下降训练的模型，并通过在小型验证集上计算样本权重来实现跨不同人口群体的模型性能平衡，从而缓解先前机器学习算法中存在的不公平问题。在合成数据集和实际数据集上的实验表明，FAIRIF 可以在牺牲很少的模型实用性的情况下实现更好的公平性 - 实用性权衡。