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arXiv · 2112.11436

Lyric document embeddings for music tagging

Matt McVicar·Bruno Di Giorgi·Baris Dundar·Matthias Mauch
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Abstract

We present an empirical study on embedding the lyrics of a song into a fixed-dimensional feature for the purpose of music tagging. Five methods of computing token-level and four methods of computing document-level representations are trained on an industrial-scale dataset of tens of millions of songs. We compare simple averaging of pretrained embeddings to modern recurrent and attention-based neural architectures. Evaluating on a wide range of tagging tasks such as genre classification, explicit content identification and era detection, we find that averaging word embeddings outperform more complex architectures in many downstream metrics.

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Matt McVicar, Bruno Di Giorgi, Baris Dundar, Matthias Mauch. 2021-11-29. Lyric document embeddings for music tagging. https://arxiv.org/abs/2112.11436

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