Significant strides have been made in creating voice identity representations
using speech data. However, the same level of progress has not been achieved
for singing voices. To bridge this gap, we suggest a framework for training
singer identity encoders to extract representations suitable for various
singing-related tasks, such as singing voice similarity and synthesis. We
explore different self-supervised learning techniques on a large collection of
isolated vocal tracks and apply data augmentations during training to ensure
that the representations are invariant to pitch and content variations. We
evaluate the quality of the resulting representations on singer similarity and
identification tasks across multiple datasets, with a particular emphasis on
out-of-domain generalization. Our proposed framework produces high-quality
embeddings that outperform both speaker verification and wav2vec 2.0
pre-trained baselines on singing voice while operating at 44.1 kHz. We release
our code and trained models to facilitate further research on singing voice and
related areas.

我们提出了一个框架，通过在大量的孤立音轨上应用不同的自监督学习技术以及数据增强，训练歌手身份编码器以提取适用于各种歌唱相关任务（如歌声相似性和合成）的表示，我们评估了产生的表示在多个数据集上进行歌手相似性和识别任务，并重点关注领域外泛化，我们的框架在 44.1 kHz 下产生高质量的嵌入，优于说话人验证和 wav2vec 2.0 预训练基线，在歌唱声音上，并发布了我们的代码和训练模型，以促进对歌唱声音和相关领域的进一步研究。