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Deep Learning for Cover Song Apperception

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1199))

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Abstract

In this work, we proposed a cover song recognition system using deep learning. From the literature, understand that most of the works extract the discriminate feature that classifies the cover song between a pair of songs and calculates the dissimilarity or similarity between the two songs based on the observation, which is a meaningful pattern between cover songs. Moreover, it inspires reformulating the cover song apperception obstacle in a machine learning framework. In other words, essentially builds the cover song recognition system using Convolution Neural Network (CNN) and Mel Frequency Cepstral Coefficients (MFCCs) features following the construction of the data set composed of cover song pairs. The prepared CNN yields the likelihood of being in the spread tune connection given a cross-closeness grid produced from any two bits of music and recognizes the spread tune by positioning on the likelihood. Test results display the prescribed methodology that has accomplished enhanced execution tantamount to the cutting edge endeavors.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, 570017, India

    D. Khasim Vali

  2. Department of Computer Science and Engineering, Government College of Engineering, Chamarajanagara, 571313, India

    Nagappa U. Bhajantri

Authors
  1. D. Khasim Vali
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  2. Nagappa U. Bhajantri
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Corresponding author

Correspondence to D. Khasim Vali .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women's University, Bhubaneswar, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Vali, D.K., Bhajantri, N.U. (2021). Deep Learning for Cover Song Apperception. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_9

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  • DOI: https://doi.org/10.1007/978-981-15-6353-9_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6352-2

  • Online ISBN: 978-981-15-6353-9

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