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

Singer Identity Representation Learning using Self-Supervised Techniques