Research on Pitch Extraction Algorithm of Noisy Speech

被引：0
|
作者：
Xing Hongyan [1 ]
Yu Cuihua [1 ]
Li Peng [1 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing, Jiangsu, Peoples R China
来源：
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词：
Hilbert-Huang transform; EMD soft-threshold de-noising method; pitch detection;
D O I：
10.4028/www.scientific.net/AMR.433-440.4675
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Pitch detection in noisy environment plays an important role in speech analyzing and recognition. In the light of the properties of Hilbert-Huang transform and the EMD soft-threshold de-noising method, an effective pitch detection method for noisy speech signal is proposed in this paper. Firstly, the EMD soft-threshold de-noising method is applied to realize the background noise reduction, secondly, using the Hilbert-Huang transform to detect the pitch period of the de-noising speech signal. The analysis proposed in this paper show that, compared with the conventional methods of the pitch detection of the noisy speech, especially for the low signal to noise ratio (SNR), this approach has a higher accuracy.
引用
收藏
页码：4675 / 4678
页数：4
相关论文
共 50 条
  • [31] Combining Zero Replacement Speech Enhancement with Lag Window Method for Pitch Detection
    Du, Sicong
    Sugiura, Yosuke
    Shimamura, Tetsuya
    2018 IEEE 3RD INTERNATIONAL CONFERENCE ON COMMUNICATION AND INFORMATION SYSTEMS (ICCIS), 2018, : 53 - 57
  • [32] Robust speech spectra restoration against unspecific noise conditions for pitch detection
    Xu, Xin
    Hayasaka, Noboru
    Miyanaga, Yoshikazu
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2008, E91A (03): : 775 - 781
  • [33] Pitch segmentation of speech signals based on short-time energy waveform
    Wiriyarattanakul S.
    Eua-anant N.
    Wiriyarattanakul, Sopon (sopon_w@kkumail.com), 1600, Springer Science and Business Media, LLC (20): : 907 - 917
  • [34] A pitch detection method for speech signals with low signal-to-noise ratio
    Shahnaz, C.
    Zhu, W. -P.
    Ahmad, M. O.
    2007 INTERNATIONAL SYMPOSIUM ON SIGNALS, SYSTEMS AND ELECTRONICS, VOLS 1 AND 2, 2007, : 386 - 389
  • [35] End-to-End Noisy Speech Recognition Using Fourier and Hilbert Spectrum Features
    Vazhenina, Daria
    Markov, Konstantin
    ELECTRONICS, 2020, 9 (07) : 1 - 18
  • [36] Performance measurement of a novel pitch detection scheme based on weighted autocorrelation for speech signals
    Sandeep Kumar
    International Journal of Speech Technology, 2019, 22 : 885 - 892
  • [37] A Neural Network-Based Optimal Nonlinear Fusion of Speech Pitch Detection Algorithms
    Imani, Ziba
    Kabudian, Seyed Jahanshah
    2019 IEEE 5TH CONFERENCE ON KNOWLEDGE BASED ENGINEERING AND INNOVATION (KBEI 2019), 2019, : 794 - 798
  • [38] Novel wavelet-based pitch estimation and segmentation of non-stationary speech
    Charalampidis, D
    Kura, VB
    2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2, 2005, : 1383 - 1387
  • [39] Performance measurement of a novel pitch detection scheme based on weighted autocorrelation for speech signals
    Kumar, Sandeep
    INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2019, 22 (04) : 885 - 892
  • [40] An effective pitch detection method for speech signals with low signal-to-noise ratio
    Zhao, Zhen-Dong
    Hu, Xi-Mei
    Tian, Jing-Feng
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 2775 - +
12345