Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Tag-based music retrieval is crucial to browse large-scale mu-sic libraries efficiently. Hence, automatic music tagging has been actively explored, mostly as a classification task, which has an inherent limitation: a fixed vocabulary. On the other hand, metric learning enables flexible vocabularies by using pretrained word embeddings as side information. Also, met-ric learning has proven its suitability for cross-modal retrieval tasks in other domains (e.g., text-to-image) by jointly learning a multimodal embedding space. In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings. Our experimental results show that the proposed ideas enhance the retrieval system quantitatively and qualitatively. Furthermore, we release the MSD500: a subset of the Million Song Dataset (MSD) containing 500 cleaned tags, 7 manually annotated tag categories, and user taste profiles.
Won M, Oramas S, Nieto O, Gouyon F, Serra X. Multimodal Metric Learning For Tag-Based Music Retrieval. In: Androutsos, Dimitri; Plataniotis, Kostas; Zhang, Xiao-Ping. Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1 ed. 2021. p. 591-595.