  Cloned voices of popular singers sound increasingly realistic and have gained
popularity over the past few years. They however pose a threat to the industry
due to personality rights concerns. As such, methods to identify the original
singer in synthetic voices are needed. In this paper, we investigate how singer
identification methods could be used for such a task. We present three
embedding models that are trained using a singer-level contrastive learning
scheme, where positive pairs consist of segments with vocals from the same
singers. These segments can be mixtures for the first model, vocals for the
second, and both for the third. We demonstrate that all three models are highly
capable of identifying real singers. However, their performance deteriorates
when classifying cloned versions of singers in our evaluation set. This is
especially true for models that use mixtures as an input. These findings
highlight the need to understand the biases that exist within singer
identification systems, and how they can influence the identification of voice
deepfakes in music.
