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Identifying singers is an important task with many applications. However, thetask remains challenging due to many issues. One major issue is related to theconfounding factors from the background instrumental music that is mixed withthe vocals in music production. A singer identification model may learn toextract non-vocal related features from the instrumental part of the songs, ifa singer only sings in certain musical contexts (e.g., genres). The modelcannot therefore generalize well when the singer sings in unseen contexts. Inthis paper, we attempt to address this issue. Specifically, we employopen-unmix, an open source tool with state-of-the-art performance in sourceseparation, to separate the vocal and instrumental tracks of music. We theninvestigate two means to train a singer identification model: by learning fromthe separated vocal only, or from an augmented set of data where we"shuffle-and-remix" the separated vocal tracks and instrumental tracks ofdifferent songs to artificially make the singers sing in different contexts. Wealso incorporate melodic features learned from the vocal melody contour forbetter performance. Evaluation results on a benchmark dataset called theartist20 shows that this data augmentation method greatly improves the accuracyof singer identification.

Addressing the confounds of accompaniments in singer identification