Papers › ByteCover: Cover Song Identification via Multi-Loss Training
ByteCover: Cover Song Identification via Multi-Loss Training
Xingjian Du, Zhesong Yu, Bilei Zhu, Xiaoou Chen, Zejun Ma
We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). ByteCover is built based on the classical ResNet model, and two major improvements are designed to further enhance the capability of the model for CSI. In the first improvement, we introduce the integration of instance normalization (IN) and batch normalization (BN) to build IBN blocks, which are major components of our ResNet-IBN model. With the help of the IBN blocks, our CSI model can learn features that are invariant to the changes of musical attributes such as key, tempo, timbre and genre, while preserving the version information. In the second improvement, we employ the BNNeck method to allow a multi-loss training and encourage our method to jointly optimize a classification loss and a triplet loss, and by this means, the inter-class discrimination and intra-class compactness of cover songs, can be ensured at the same time. A set of experiments demonstrated the effectiveness and efficiency of ByteCover on multiple datasets, and in the Da-TACOS dataset, ByteCover outperformed the best competitive system by 20.9\%.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cover song identification | Covers80 | ByteCover | MAP | 0.906 | #3 of 5 | Archive leaderboard | report |
| Cover song identification | Da-TACOS | ByteCover | mAP | 0.743 | #2 of 4 | Archive leaderboard | report |
| Cover song identification | SHS100K-TEST | ByteCover | mAP | 0.836 | #4 of 8 | Archive leaderboard | report |
| Cover song identification | YouTube350 | ByteCover | MAP | 0.955 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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