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Utilization of windowing effect and accumulated autocorrelation function and power spectrum for pitch detection in noisy environments
Author(s) -
Rahman Md. Saifur,
Sugiura Yosuke,
Shimamura Tetsuya
Publication year - 2020
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23238
Subject(s) - window function , autocorrelation , pitch detection algorithm , computer science , spectral density , noise (video) , speech recognition , window (computing) , variety (cybernetics) , electronic engineering , artificial intelligence , engineering , speech processing , mathematics , telecommunications , statistics , image (mathematics) , operating system
In this paper, considering a progressing trend of recent techniques for pitch detection of speech in noisy environments, windowing effects are discussed analytically, and it is insisted that the Rectangular window should be proactively used instead of the popular Hanning or Hamming window. In a variety of noise environments, a performance comparison of the conventional pitch detection methods is conducted, and as a result, we take a standpoint to support the autocorrelation (ACF) method. Incorporating accumulation techniques, three types of pitch detection approaches are developed. Through experiments, it is shown that the three accumulation based approaches have the potential to provide better performance than recent state‐of‐the art methods for pitch detection without relying on a complicated post processing technique. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.