@inproceedings{3bacc7d5513c4fce8ff0d24a22d740e0,
title = "Cover Song Identification in Practice with Multimodal Co-Training",
abstract = "The task of cover song identification (CSI) deals with the automatic matching of audio recordings by modeling musical similarity. CSI is of high relevance in the context of applications such as copyright infringement detection on online video platforms. Since online videos include metadata (eg. video titles, descriptions), one could leverage it for more effective CSI in practice. In this work, we experiment with state-of-the-art models of CSI and entity matching in a Co-Training ensemble. Our results outline slight improvements of the entity matching model. We further outline some suggestions for improvements of our approach to overcome the issue of overfitting CSI models which we observed.",
keywords = "co-training, cover song identification, entity matching",
author = "Simon Hachmeier and Robert J{\"a}schke",
year = "2023",
language = "English",
volume = "3630",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop",
pages = "359--371",
booktitle = "LWDA 2023",
address = "Germany",
note = "2023 Learning. Knowledge. Data. Analytics, LWDA 2023 ; Conference date: 09-10-2023 Through 11-10-2023",
}