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Deep feature learning for cover song identification

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Abstract

The identification of a cover song, which is an alternative version of a previously recorded song, for music retrieval has received increasing attention. Methods for identifying a cover song typically involve comparing the similarity of chroma features between a query song and another song in the data set. However, considerable time is required for pairwise comparisons. In this study, chroma features were patched to preserve the melody. An intermediate representation was trained to reduce the dimension of each patch of chroma features. The training was performed using an autoencoder, commonly used in deep learning for dimensionality reduction. Experimental results showed that the proposed method achieved better accuracy for identification and spent less time for similarity matching in both covers80 dataset and Million Song Dataset as compared with traditional approaches.

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Authors and Affiliations

  1. Department of Electronic Engineering, Ming Chuan University, No.5, Deming Rd, Taoyuan, 33348, Taiwan

    Jiunn-Tsair Fang

  2. Department of Communication Engineering, National Central University, No.300, Jhongda Rd, Taoyuan, 32001, Taiwan

    Chi-Ting Day & Pao-Chi Chang

Authors
  1. Jiunn-Tsair Fang
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  2. Chi-Ting Day
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  3. Pao-Chi Chang
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Correspondence to Pao-Chi Chang.

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Fang, JT., Day, CT. & Chang, PC. Deep feature learning for cover song identification. Multimed Tools Appl 76, 23225–23238 (2017). https://doi.org/10.1007/s11042-016-4107-6

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  • DOI: https://doi.org/10.1007/s11042-016-4107-6

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