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WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification

18 Jul 2022Interspeech 2022 7archive 2025-07-28

Shichao Hu, Bin Zhang, JinHong Lu, Yiliang Jiang, Wucheng Wang, Lingcheng Kong, Weifeng Zhao, Tao Jiang

Cover song identification~(CSI) has been a challenging task and an import topic in music information retrieval~(MIR) community. In recent years, CSI problems have been extensively studied based on deep learning methods. In this paper, we propose a novel framework for CSI based on a joint representation learning method inspired by multi-task learning. In specific, we propose a joint learning strategy which combines classification and metric learning for optimizing the cover song model based on WideResNet, called LyraC-Net. Classification objective learns separable embeddings from different classes, while metric learning optimizes embedding similarity by decreasing the inter-class distance and increasing the intra-classs separability. This joint optimization strategy is expected to learn a more robust cover song representation than methods with single training objectives. For the metric learning, prototypical network is introduced to stabilize and accelerate the training process, together with triplet loss. Furthermore, we introduce SpecAugment, a popular augmentation method in speech recognition, to further improve the performance. Experiment results show that our proposed method achieves promising results and outperforms other recent CSI methods in the evaluations.

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Tasks

Cover song identification Data Augmentation Information Retrieval Metric Learning Multi-Task Learning Music Information Retrieval Representation Learning Speech Recognition speech-recognition

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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	SHS100K-TEST	LyraC-Net	mAP	0.765	#6 of 8	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.

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