@inproceedings{14e9323216154f718d4f8ddd13877f7d,
title = "Learning and feature extraction based fundamental frequency determination algorithm in very low SNR scenario",
abstract = "Fundamental frequency determination is critical for music and radar signal analysis. In practice, the fundamental frequency is hard to be determined precisely especially when the signal-to-noise ratio (SNR) is low. In this paper, we propose an algorithm using both feature extraction and machine learning to determine fundamental frequency precisely. First, several features, including the correlation in the time-frequency domain and the differences to the previous/ next local minima, are extracted. Then, a learning-based classifier is applied. The proposed algorithm can estimate the fundamental frequency accurately even when the SNR is about -9dB and the signal length is only 4 seconds.",
keywords = "Feature extraction, Fundamental frequency, Machine learning, Radar signal, Spectrum analysis",
author = "Hua, {Shiang Chih} and Ding, {Jian Jiun} and Wang, {Chih Hao} and Ouyang, {Liang Yu} and Huang, {Jin Yu}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 ; Conference date: 10-10-2020 Through 21-10-2020",
year = "2020",
language = "???core.languages.en_GB???",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings",
}