A Study of Peak Finding Algorithms for the Autocorrelation Function of Speech Signal

Sin-Ae So; Kanghee Lee; Kwang-Bock You; 임하영; 박지수

In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed. According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics – such as monotonic increasing function - of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing. The speech data, is composed of Korean emotion expressed words, is used for evaluation of their performance. The pitches are measured to compare the performance of proposed algorithms.

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* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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이 논문은 한국연구재단의 지원을 받아 작성되었습니다.

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