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Query by Humming for Song Identification Using Voice Isolation

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12799))

Abstract

There are some methods for searching in large music databases, like searching by song name or artist name. However, in some cases these methods are not enough. For instance, a person might not remember the name of a song, but might remember its melody. In Music Information Retrieval, there is a task called Query-By-Humming, which allows retrieving a rank of songs that are similar to an audio humming. In this research, we propose the use of vocal isolation methods to improve query-by-humming systems. To achieve this, different configurations of Query-by-Humming systems were tested to analyze the results and determine in which cases our proposal works better. The results showed that vocal isolation improves the performance of Query-by-Humming systems when the music collection consists of modern songs.

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Notes

  1. 1.

    Music Business Worldwide - https://bit.ly/37TQNE4.

  2. 2.

    Music Technology Group - https://bit.ly/2IdElnG.

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Authors and Affiliations

  1. Universidad Peruana de Ciencias Aplicadas, Lima, Peru

    Edwin Alfaro-Paredes, Leonardo Alfaro-Carrasco & Willy Ugarte

Authors
  1. Edwin Alfaro-Paredes
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  2. Leonardo Alfaro-Carrasco
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  3. Willy Ugarte
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Corresponding author

Correspondence to Willy Ugarte .

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Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Moonis Ali

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Alfaro-Paredes, E., Alfaro-Carrasco, L., Ugarte, W. (2021). Query by Humming for Song Identification Using Voice Isolation. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_27

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  • DOI: https://doi.org/10.1007/978-3-030-79463-7_27

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