Automatic identification of bird species based on sinusoidal modeling of syllables

被引:0
|
作者
Härmä, A [1 ]
机构
[1] Helsinki Univ Technol, Lab Acoust & Audio Signal Proc, FIN-02015 Espoo, Finland
关键词
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Syllables are elementary building blocks of bird song. In sounds of many songbirds a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. In this article we test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that with limited sets of bird species a recognizer based on this signal model may already be sufficient.
引用
收藏
页码:545 / 548
页数:4
相关论文
共 50 条
  • [31] Normalized approximate descent used for spike based automatic bird species recognition system
    Mohanty, Ricky
    Mallik, Bandi Kumar
    Solanki, Sandeep Singh
    INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2022, 25 (01) : 57 - 65
  • [32] Sound-spectrogram based automatic bird species recognition using MLP classifier
    Pahuja, Roop
    Kumar, Avijeet
    APPLIED ACOUSTICS, 2021, 180
  • [33] Automatic Frequency Feature Extraction for Bird Species Delimitation
    O'Reilly, Colm
    Kokuer, Munevver
    Jancovic, Peter
    Drennan, Regan
    Harte, Naomi
    2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2017, : 1759 - 1763
  • [34] Deep Learning Case Study for Automatic Bird Identification
    Niemi, Juha
    Tanttu, Juha T.
    APPLIED SCIENCES-BASEL, 2018, 8 (11):
  • [35] Deep learning-based automatic bird identification system for offshore wind farms
    Niemi, Juha
    Tanttu, Juha T.
    WIND ENERGY, 2020, 23 (06) : 1394 - 1407
  • [36] Automatic feature-queried bird identification system based on entropy and fuzzy similarity
    Wang, Xingqi
    Schiner, Thorsten
    Yao, Xin
    EXPERT SYSTEMS WITH APPLICATIONS, 2008, 34 (04) : 2879 - 2884
  • [37] Automatic identification of bird females using egg phenotype
    Sulc, Michal
    Hughes, Anna E.
    Troscianko, Jolyon
    Stetkova, Gabriela
    Prochazka, Petr
    Pozgayova, Milica
    Pialek, Lubomir
    Pialkova, Radka
    Brlik, Vojtech
    Honza, Marcel
    ZOOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, 2022, 195 (01) : 33 - 44
  • [38] An automatic identification method of common species based on ensemble learning
    Li, Hao-Xuan
    Zhang, Mei
    Meng, De-Yao
    Geng, Bo
    Li, Zu-Kui
    Huang, Chuan-Feng
    Li, Wen-Kang
    Jiang, Han-Lin
    Wu, Rong-Hai
    Li, Xiao-Wei
    Chen, Ben-Hui
    Yang, Deng-Qi
    Ren, Guo-Peng
    ECOLOGICAL INFORMATICS, 2025, 86
  • [39] Automatic Fault Identification in Sensor Networks Based on Probabilistic Modeling
    Ntalampiras, Stavros
    Giannopoulos, Georgios
    CRITICAL INFORMATION INFRASTRUCTURES SECURITY (CRITIS 2014), 2016, 8985 : 344 - 354
  • [40] An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features
    Wang, Hanlin
    Xu, Yingfan
    Yu, Yan
    Lin, Yucheng
    Ran, Jianghong
    ANIMALS, 2022, 12 (18):