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Fast, simple, accurate Fundamental Frequency estimation

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Abstract

This paper presents a simple, fast, accurate Fundamental Frequency estimation method with good resistance to Harmonics and noise. In the proposed method named the Accumulated sum method (ASM), a new data sample of a measured signal is constructed using the summation of the samples in the original sampling data. The effects of Harmonics and noise are mitigated by the proposed algorithm. Performance comparison of the proposed method, the method based on the discrete Fourier, and the accurate method named shifting window average method (SWAM) is demonstrated by using simulated signals with varied fundamental frequency from 45 Hz to 65 Hz and with additive Harmonics and noise. In the simulation, the sampling frequency and the number of samples is fixed to 5000 samples/s and 100, respectively. From the simulation results, the proposed method exhibits good immunity to Harmonics and noise. Due to its low required processing effort and high precision, this method is a good candidate for online Frequency estimation in the presence of Harmonics and Noise signals.

Fundamental frequency estimation (2 items found) | Accumulated sum method (1 items found) | Performance comparison (65 items found) | Frequency estimation (57 items found) | Noise signals (8 items found) | Harmonics (35 items found) | Noise signalFast Fourier transforms | Shifting window averages | Fundamental frequencies | Fast Fourier transform | Sampling frequencies | Natural frequencies | Harmonic analysis |

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