A Review of Automated Bioacoustics and General Acoustics Classification Research

被引:6
作者
Mutanu, Leah [1 ]
Gohil, Jeet [1 ]
Gupta, Khushi [2 ]
Wagio, Perpetua [1 ]
Kotonya, Gerald [3 ]
机构
[1] US Int Univ Africa, Dept Comp, POB 14634-0800, Nairobi, Kenya
[2] Sam Houston State Univ, Dept Comp Sci, Huntsville, TX 77341 USA
[3] Univ Lancaster, Sch Comp & Commun, Lacaster LA1 4WA, England
关键词
sound classification; bioacoustics; survey; review; acoustic detection; general acoustics; ENVIRONMENTAL SOUND CLASSIFICATION; NEURAL-NETWORK; MACHINE; RECOGNITION; AUDIO; IDENTIFICATION; METHODOLOGY; IDENTITY; FEATURES; SPEECH;
D O I
10.3390/s22218361
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Automated bioacoustics classification has received increasing attention from the research community in recent years due its cross-disciplinary nature and its diverse application. Applications in bioacoustics classification range from smart acoustic sensor networks that investigate the effects of acoustic vocalizations on species to context-aware edge devices that anticipate changes in their environment adapt their sensing and processing accordingly. The research described here is an in-depth survey of the current state of bioacoustics classification and monitoring. The survey examines bioacoustics classification alongside general acoustics to provide a representative picture of the research landscape. The survey reviewed 124 studies spanning eight years of research. The survey identifies the key application areas in bioacoustics research and the techniques used in audio transformation and feature extraction. The survey also examines the classification algorithms used in bioacoustics systems. Lastly, the survey examines current challenges, possible opportunities, and future directions in bioacoustics.
引用
收藏
页数:26
相关论文
共 160 条
  • [21] An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning
    Bjerge, Kim
    Nielsen, Jakob Bonde
    Sepstrup, Martin Videbaek
    Helsing-Nielsen, Flemming
    Hoye, Toke Thomas
    [J]. SENSORS, 2021, 21 (02) : 1 - 18
  • [22] A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments
    Bonet-Sola, Daniel
    Alsina-Pages, Rosa Ma
    [J]. SENSORS, 2021, 21 (04) : 1 - 21
  • [23] Machine Learning Algorithms for Environmental Sound Recognition: Towards Soundscape Semantics
    Bountourakis, Vasileios
    Vrysis, Lazaros
    Papanikolaou, George
    [J]. PROCEEDINGS OF THE 10TH AUDIO MOSTLY: A CONFERENCE ON INTERACTION WITH SOUND, AM'15, 2015,
  • [24] A Personalizable Mobile Sound Detector App Design for Deaf and Hard-of-Hearing Users
    Bragg, Danielle
    Huynh, Nicholas
    Ladner, Richard E.
    [J]. ASSETS'16: PROCEEDINGS OF THE 18TH INTERNATIONAL ACM SIGACCESS CONFERENCE ON COMPUTERS AND ACCESSIBILITY, 2016, : 3 - 13
  • [25] Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture
    Bravo Sanchez, Francisco J.
    Hossain, Md Rahat
    English, Nathan B.
    Moore, Steven T.
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)
  • [26] Automatic Detection and Monitoring of Insect Pests-A Review
    Cardim Ferreira Lima, Matheus
    Damascena de Almeida Leandro, Maria Elisa
    Valero, Constantino
    Pereira Coronel, Luis Carlos
    Goncalves Bazzo, Clara Oliva
    [J]. AGRICULTURE-BASEL, 2020, 10 (05):
  • [27] Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques
    Caruso, Francesco
    Dong, Lijun
    Lin, Mingli
    Liu, Mingming
    Gong, Zining
    Xu, Wanxue
    Aionge, Giuseppe
    Li, Songhai
    [J]. FRONTIERS IN MARINE SCIENCE, 2020, 7
  • [28] Environmental sound recognition: a survey
    Chachada, Sachin
    Kuo, C. -C. Jay
    [J]. APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, 2014, 3
  • [29] Chalmers C., 2021, P 2021 INT JOINT C N, P1, DOI [10.1109/IJCNN52387.2021.9534195, DOI 10.1109/IJCNN52387.2021.9534195]
  • [30] Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics
    Chao, Kuo-Wei
    Hu, Nian-Ze
    Chao, Yi-Chu
    Su, Chin-Kai
    Chiu, Wei-Hang
    [J]. SYMMETRY-BASEL, 2019, 11 (12):