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 Improving autocorrelation algorithm for detecting fundamental frequency of guitar signals on Arm Cortex-A9 processor
Tác giả hoặc Nhóm tác giả: Nguyen Binh Thien, Ninh Khanh Duy
Nơi đăng: Proceedings of 2017 Joint Academic Forum on Danang, The University of Danang - University of Science and Technology with Japan University & Company Research Group cooperated by IEEE; Số: 2017;Từ->đến trang: 158-162;Năm: 2017
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
Detecting the fundamental frequency of signal is a common issue in audio signal processing, especially music processing. This paper implements pitch detection algorithm for guitar signals using autocorrelation function on Arm Cortex-A9 processor. To increase the accuracy of the algorithm, Gaussian filter and cubic spline interpolation were used. Experiments on guitar signals show that the root mean square errors of the autocorrelation algorithms with and without using cubic spline interpolation compared to the RAPT algorithm are 1.0147 Hz and 1.1199 Hz, respectively. Experimetal results also exhibit that the use of cubic spline interpolation has more effectiveness when the estimated fundamental frequency is higher. Finally, we have built a prototype of a guitar tuner device using Renesas’s GR-PEACH boards for demonstrating the research results.
ABSTRACT
Detecting the fundamental frequency of signal is a common issue in audio signal processing, especially music processing. This paper implements pitch detection algorithm for guitar signals using autocorrelation function on Arm Cortex-A9 processor. To increase the accuracy of the algorithm, Gaussian filter and cubic spline interpolation were used. Experiments on guitar signals show that the root mean square errors of the autocorrelation algorithms with and without using cubic spline interpolation compared to the RAPT algorithm are 1.0147 Hz and 1.1199 Hz, respectively. Experimetal results also exhibit that the use of cubic spline interpolation has more effectiveness when the estimated fundamental frequency is higher. Finally, we have built a prototype of a guitar tuner device using Renesas’s GR-PEACH boards for demonstrating the research results.
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