Pitch extraction by using autocorrelation function on the log spectrum

Kunieda, Nobuyuki; Shimamura, Tetsuya; Suzuki, Jun

doi:10.1002/(sici)1520-6440(200001)83:1<90::aid-ecjc11>3.0.co;2-9

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2024

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Cited by 11 publications

References 13 publications

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Utilization of windowing effect and accumulated autocorrelation function and power spectrum for pitch detection in noisy environments

Rahman

Sugiura

Shimamura

2020

IEEJ Transactions Elec Engng

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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.

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