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    An investigation into several pitch detection algorithms for singing phrases analysis


    Faghih, Behnam and Timoney, Joseph (2019) An investigation into several pitch detection algorithms for singing phrases analysis. In: 2019 30th Irish Signals and Systems Conference (ISSC). IEEE. ISBN 9781728128009

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    Abstract

    This article provides an investigation into four different pitch detection algorithms: pYin, Praat, Phase Lock Loops (PLL)-based, and an Extended Complex Kalman Filter approach. The first two algorithms compute the pitch on a frame-by-frame basis while the latter two work on a sample-bysample basis. Only in recent years has there been a noticeable increase in the number of papers applying pitch detection techniques to sung phrases. This investigation is done on a dataset contained 76 files of singers. To create a ground truth from the data an alternative approach using the Spear analysis program is applied. The algorithms are compared using a new Singing Data Analyser tool. It was observed that the pYin and Praat are the most reliable algorithms while the PLL and Kalman filter algorithms are very dependent on the userselected parameters.
    Item Type: Book Section
    Additional Information: Cite as: B. Faghih and J. Timoney, "An investigation into several pitch detection algorithms for singing phrases analysis," 2019 30th Irish Signals and Systems Conference (ISSC), Maynooth, Ireland, 2019, pp. 1-5, doi: 10.1109/ISSC.2019.8904943.
    Keywords: Pitch detection; singing analysis; PLL; Kalman filter;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 14192
    Identification Number: 10.1109/ISSC.2019.8904943
    Depositing User: Joseph Timoney
    Date Deposited: 16 Mar 2021 14:32
    Publisher: IEEE
    Refereed: Yes
    Related URLs:
    URI: https://mural.maynoothuniversity.ie/id/eprint/14192
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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