Advancements and prospects of software for processing and analyzing terrestrial mammal sound data
Received date: 2024-12-06
Accepted date: 2025-04-21
Online published: 2025-12-03
Terrestrial mammal vocalization data processing and analysis software is an essential tool for ecological researchers, enabling automated and efficient handling and analysis of bioacoustic recordings. These tools are vital for delving into the intricate details of animal vocalizations and providing foundational support for ecoacoustic and animal vocalization studies. The rapid advancement of passive acoustic monitoring technology and artificial intelligence has led to a significant increase in the number and variety of vocalization analysis software. Recent developments have seen substantial progress in noise reduction, acoustic indices, data annotation, species identification, and feature extraction. These softwares play a crucial role in archiving bioacoustic data, ensuring the preservation and accessibility of valuable ecological information for long-term monitoring and research purposes. This paper systematically reviews relevant literature and open-source software platforms, outlining the workflow for processing and analyzing terrestrial mammal vocalization data. It aims to discuss the current state of vocalization analysis software and its applications in bioacoustic research, and provides an outlook on the future development and localization of AI-assisted bioacoustic software to enhance analysis efficiency and broaden its applicability.
Yongqiao HUANG , Chengyun ZHANG , Zixin ZHANG , Zezhou HAO . Advancements and prospects of software for processing and analyzing terrestrial mammal sound data[J]. ACTA THERIOLOGICA SINICA, 2025 , 45(6) : 784 -796 . DOI: 10.16829/j.slxb.151036
| Alcocer I, Lima H, Sugai L S M, Llusia D. 2022. Acoustic indices as proxies for biodiversity: a meta‐analysis [J]. Biological Reviews, 97 (6): 2209-2236. | |
| Allen?Ankins S, McKnight D T, Nordberg E J, Hoefer S, Roe P, Watson D M, McDonald P G, Fuller R A, Schwarzkopf L. 2023. Effectiveness of acoustic indices as indicators of vertebrate biodiversity [J]. Ecological Indicators, 147: 109937. DOI: 10.1016/j.ecolind.2023.109937 . | |
| Barroso V R, Xavier F C, Ferreira C E L. 2023. Applications of machine learning to identify and characterize the sounds produced by fish [J]. ICES Journal of Marine Science, 80 (7): 1854-1867. | |
| Bonet?Solà D, Alsina?Pagès R M. 2021. A comparative survey of feature extraction and machine learning methods in diverse acoustic environments [J]. Sensors, 21 (4): 1274. DOI: 10.3390/s21041274 . | |
| Bota G, Manzano?Rubio R, Catalán L, Gómez?Catasús J, Pérez?Granados C. 2023. Hearing to the unseen: AudioMoth and BirdNET as a cheap and easy method for monitoring cryptic bird species [J]. Sensors, 23 (16): 7176. DOI: 10.3390/s23167176 . | |
| Bota G, Manzano?Rubio R, Fanlo H, Franch N, Brotons L, Villero D, Devisscher S, Pavesi A, Cavaletti E, Pérez?Granados C. 2024. Passive acoustic monitoring and automated detection of the American bullfrog [J]. Biological Invasions, 26 (4): 1269-1279. | |
| Brabant R, Laurent Y, Dolap U, Degraer S, Poerink B. 2018. Comparing the results of four widely used automated bat identification software programs to identify nine bat species in coastal Western Europe [J]. Belgian Journal of Zoology, 148 (2). DOI: 10.26496/bjz.2018.21 . | |
| Bradfer?Lawrence T, Bunnefeld N, Gardner N, Willis S G, Dent D H. 2020. Rapid assessment of avian species richness and abundance using acoustic indices [J]. Ecological Indicators, 115: 106400. DOI: 10.1016/j.ecolind.2020.106400 . | |
| Brinkl?v S M M, Macaulay J, Bergler C, Tougaard J, Beedholm K, Elmeros M, Madsen P T. 2023. Open‐source workflow approaches to passive acoustic monitoring of bats [J]. Methods in Ecology and Evolution, 14 (7): 1747-1763. | |
| Brooker S A, Stephens P A, Whittingham M J, Willis S G. 2020. Automated detection and classification of birdsong: an ensemble approach [J]. Ecological Indicators, 117: 106609. DOI: 10.1016/j.ecolind.2020.106609 . | |
| Brown A, Garg S, Montgomery J. 2017. Automatic and efficient denoising of bioacoustics recordings using mmse stsa [J]. IEEE Access, 6: 5010-5022. | |
| Brown C, Riede T. 2017. Comparative Bioacoustics: An Overview [M]. Bentham Science Publishers. | |
| Buxton R T, McKenna M F, Clapp M, Meyer E, Stabenau E, Angeloni L M, Crooks K, Wittemyer G. 2018. Efficacy of extracting indices from large?scale acoustic recordings to monitor biodiversity [J]. Conservation Biology, 32 (5): 1174-1184. | |
| Campos I B, Fewster R, Landers T, Truskinger A, Towsey M, Roe P, Lee W, Gaskett A. 2022. Acoustic region workflow for efficient comparison of soundscapes under different invasive mammals’ management regimes [J]. Ecological Informatics, 68: 101554. DOI: 10.1016/j.ecoinf.2022.101554 . | |
| Caouette A P, Bayne E M, Judge K A. 2024. Large‐scale bioacoustic monitoring to elucidate the distribution of a non?native katydid [J]. Ecological Entomology, 49 (1): 119-127. | |
| Digby A, Towsey M, Bell B D, Teal P D. 2013. A practical comparison of manual and autonomous methods for acoustic monitoring [J]. Methods in Ecology and Evolution, 4 (7): 675-683. | |
| Dufourq E, Durbach I, Hansford J P, Hoepfner A, Ma H, Bryant J V, Stender C S, Li W, Liu Z, Chen Q. 2021. Automated detection of Hainan gibbon calls for passive acoustic monitoring [J]. Remote Sensing in Ecology and Conservation, 7 (3): 475-487. | |
| Fairbrass A J, Firman M, Williams C, Brostow G J, Titheridge H, Jones K E. 2019. CityNet－Deep learning tools for urban ecoacoustic assessment [J]. Methods in Ecology and Evolution, 10 (2): 186-197. | |
| Fern M, Barlow R, Slootmaker C, Kush J, Shwiff S, Teeter L, Armstrong J. 2021. Economic estimates of wild hog (Sus scrofa) damage and control among young forest plantations in Alabama [J]. Small-Scale Forestry, 20: 503-516. | |
| Gibb R, Browning E, Glover‐Kapfer P, Jones K E. 2019. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring [J]. Methods in Ecology and Evolution, 10 (2): 169-185. DOI: 10.1111/2041-210X.13101 . | |
| He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778. | |
| Hensel A L J, Dobney S L, Moran I G, Thomas I P, Burant J B, Woodworth B K, Doucet S M, Newman A E M, Norris D R, Williams H, Mennill D J. 2022. Passive acoustic monitoring provides predictable and reliable underestimates of population size and longevity in wild Savannah Sparrows [J]. Ornithological Applications, 124 (3): duac018. DOI: 10.1093/ornithapp/duac018 . | |
| Hopkins J M, Edwards W, Schwarzkopf L. 2022. Invading the soundscape: exploring the effects of invasive species’ calls on acoustic signals of native wildlife [J]. Biological Invasions, 24 (11): 3381-3393. | |
| Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt D F, Weber J, Webb G I, Idoumghar L, Muller P?A, Petitjean F. 2020. Inceptiontime: finding alexnet for time series classification [J]. Data Mining and Knowledge Discovery, 34 (6): 1936-1962. | |
| Kahl S, Wood C M, Eibl M, Klinck H. 2021. BirdNET: A deep learning solution for avian diversity monitoring [J]. Ecological Informatics, 61: 101236. DOI: 10.1016/j.ecoinf.2021.101236 . | |
| Kim C?I, Cho Y, Jung S, Rew J, Hwang E. 2020. Animal sounds classification scheme based on multi?feature network with mixed datasets [J]. KSII Transactions on Internet and Information Systems (TIIS), 14 (8): 3384-3398. | |
| Laiolo P. 2010. The emerging significance of bioacoustics in animal species conservation [J]. Biological Conservation, 143 (7): 1635-1645. | |
| Lausen C L, Falxa G A, Solick D I, McEwan A L, Baker M D, de Freitas E, Sarell M. 2023. Singing silver?haired bats (Lasionycteris noctivagans) [J]. Wildlife Society Bulletin, 47 (4): e1500. DOI: 10.1002/wsb.1500 . | |
| Layng A M, Adams A M, Goertz D E, Morrison K W, Pond B A, Phoenix R D. 2019. Bat species distribution and habitat associations in northern Ontario, Canada [J]. Journal of Mammalogy, 100 (1): 249-260. | |
| Lostanlen V, Salamon J, Farnsworth A, Kelling S, Bello J P. 2019. Robust sound event detection in bioacoustic sensor networks [J]. PLoS ONE, 14 (10): e0214168. DOI: 10.1371/journal.pone.0214168 . | |
| Mammides C, Goodale E, Dayananda S K, Kang L, Chen J. 2017. Do acoustic indices correlate with bird diversity? Insights from two biodiverse regions in Yunnan Province, south China [J]. Ecological Indicators, 82: 470-477. | |
| McGinn K, Kahl S, Peery M Z, Klinck H, Wood C M. 2023. Feature embeddings from the BirdNET algorithm provide insights into avian ecology [J]. Ecological Informatics, 74: 101995. DOI: 10.1016/j.ecoinf.2023.101995 . | |
| Mena J L, Rivero J, Bonifaz E, Pastor P, Pacheco J, Aide T M. 2022. The effect of artificial light on bat richness and nocturnal soundscapes along an urbanization gradient in an arid landscape of central Peru [J]. Urban Ecosystems, 25 (2): 563-574. | |
| Metcalf O, Abrahams C, Ashington B, Baker E, Bradfer?Lawrence T, Browning E, Carruthers?Jones J, Darby J, Dick J, Eldridge A. 2023. Good practice guidelines for long?term ecoacoustic monitoring in the UK [R]. UK Acoustics Network. https://e?space.mmu.ac.uk/id/eprint/631466. | |
| Metcalf O C, Barlow J, Devenish C, Marsden S, Berenguer E, Lees A C. 2021. Acoustic indices perform better when applied at ecologically meaningful time and frequency scales [J]. Methods in Ecology and Evolution, 12 (3): 421-431. | |
| Morgan M M, Braasch J. 2022. Open set classification strategies for long?term environmental field recordings for bird species recognition [J]. The Journal of the Acoustical Society of America, 151 (6): 4028-4038. | |
| Morrison C A, Auni?? A, Benk? Z, Brotons L, Chodkiewicz T, Chylarecki P, Escandell V, Eskildsen D, Gamero A, Herrando S. 2021. Bird population declines and species turnover are changing the acoustic properties of spring soundscapes [J]. Nature Communications, 12 (1): 6217. DOI: 0.1038/s41467-021-26488-1 . | |
| Müller J, Mitesser O, Schaefer H M, Seibold S, Busse A, Kriegel P, Rabl D, Gelis R, Arteaga A, Freile J. 2023. Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests [J]. Nature Communications, 14 (1): 6191.DOI: 10.1038/s41467-021-26488-1 . | |
| Nanni L, Maguolo G, Paci M. 2020. Data augmentation approaches for improving animal audio classification [J]. Ecological Informatics, 57: 101084. DOI: 10.1016/j.ecoinf.2020.101084 . | |
| Nolasco I, Singh S, Morfi V, Lostanlen V, Strandburg?Peshkin A, Vida?a?Vila E, Gill L, Pamu?a H, Whitehead H, Kiskin I. 2023. Learning to detect an animal sound from five examples [J]. Ecological Informatics, 77: 102258. DOI: 10.1016/j.ecoinf.2023.102258 . | |
| Ntalampiras S. 2018. Bird species identification via transfer learning from music genres [J]. Ecological Informatics, 44: 76-81. | |
| Parcollet T, Morchid M, Linares G. 2020. E2E?SINCNET: Toward fully end?to?end speech recognition [C]. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7714-7718. | |
| Penar W, Magiera A, Klocek C. 2020. Applications of bioacoustics in animal ecology [J]. Ecological Complexity, 43: 100847. DOI: 10.1016/j.ecocom.2020.100847 . | |
| Pérez?Granados C, Schuchmann K?L. 2023. The sound of the illegal: Applying bioacoustics for long?term monitoring of illegal cattle in protected areas [J]. Ecological Informatics, 74: 101981. DOI: 10.1016/j.ecoinf.2023.101981 . | |
| Pérez‐Granados C. 2023. BirdNET: applications, performance, pitfalls and future opportunities [J]. Ibis, 165 (3): 1068-1075. | |
| Pieretti N, Farina A, Morri D. 2011. A new methodology to infer the singing activity of an avian community: the acoustic complexity index (ACI) [J]. Ecological Indicators, 11 (3): 868-873. | |
| Pijanowski B C, Villanueva?Rivera L J, Dumyahn S L, Farina A, Krause B L, Napoletano B M, Gage S H, Pieretti N. 2011. Soundscape ecology: the science of sound in the landscape [J]. BioScience, 61 (3): 203-216. | |
| Priyadarshani N, Marsland S, Castro I. 2018. Automated birdsong recognition in complex acoustic environments: a review [J]. Journal of Avian Biology, 49 (5): jav?01447. DOI: 10.1111/jav.01447 . | |
| Priyadarshani N, Marsland S, Juodakis J, Castro I, Listanti V. 2020. Wavelet filters for automated recognition of birdsong in long‐time field recordings [J]. Methods in Ecology and Evolution, 11 (3): 403-417. | |
| Ribeiro J, José W, Harmon K, Leite G A, de Melo T N, LeBien J, Campos?Cerqueira M. 2022. Passive acoustic monitoring as a tool to investigate the spatial distribution of invasive alien species [J]. Remote Sensing, 14 (18): 4565.DOI: 10.3390/rs14184565 . | |
| Roch M A. 2021. How machine learning contributes to solve acoustical problems [J]. Acoustics Today, 17 (4): 48. DOI: 10.1121/at.2021.17.4.48 . | |
| Rocha L H, Ferreira L S, Paula B C, Rodrigues F H, Sousa?Lima R S. 2015. An evaluation of manual and automated methods for detecting sounds of maned wolves (Chrysocyon brachyurus Illiger 1815) [J]. Bioacoustics, 24 (2): 185-198. | |
| Scarpelli M D, Liquet B, Tucker D, Fuller S, Roe P. 2021. Multi?index ecoacoustics analysis for terrestrial soundscapes: a new semi?automated approach using time?series motif discovery and random forest classification [J]. Frontiers in Ecology and Evolution, 9: 738537. DOI: 10.3389/fevo.2021.738537 . | |
| Sethi S S, Jones N S, Fulcher B D, Picinali L, Clink D J, Klinck H, Orme C D L, Wrege P H, Ewers R M. 2020. Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set [J]. Proceedings of the National Academy of Sciences, 117 (29): 17049-17055. | |
| Shamon H, Paraskevopoulou Z, Kitzes J, Card E, Deichmann J L, Boyce A J, McShea W J. 2021. Using ecoacoustics metrices to track grassland bird richness across landscape gradients [J]. Ecological Indicators, 120: 106928. DOI: 10.1016/j.ecolind.2020.106928 . | |
| Sharma G, Umapathy K, Krishnan S. 2020. Trends in audio signal feature extraction methods [J]. Applied Acoustics, 158: 107020. DOI: 10.1111/2041-210X.13521 . | |
| Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large?scale image recognition [Z]. arXiv, arXiv:1409.1556. DOI: 10.48550/arXiv.1409.1556 . | |
| Steinfath E, Palacios?Mu?oz A, Rottsch?fer J R, Yuezak D, Clemens J. 2021. Fast and accurate annotation of acoustic signals with deep neural networks [J]. Elife, 10: e68837. DOI: 10.7554/eLife.68837 . | |
| Stowell D. 2022. Computational bioacoustics with deep learning: a review and roadmap [J]. PeerJ, 10: e13152. DOI: 10.7717/peerj.13152 . | |
| Stowell D, Plumbley M D. 2014. Automatic large?scale classification of bird sounds is strongly improved by unsupervised feature learning [J]. PeerJ, 2: e488. DOI: 10.7717/peerj.488 . | |
| Stowell D, Wood M D, Pamu?a H, Stylianou Y, Glotin H. 2019. Automatic acoustic detection of birds through deep learning: The first bird audio detection challenge [J]. Methods in Ecology and Evolution, 10 (3): 368-380. | |
| Sugai L S M, Silva T S F, Ribeiro J W, Llusia D. 2019. Terrestrial passive acoustic monitoring: review and perspectives [J]. BioScience, 69 (1): 15-25. | |
| Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutions [C]. Proceedings of the IEEE conference on computer vision and pattern recognition.DOI: 10.1109/CVPR.2015.7298594 . | |
| Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2016. Rethinking the inception architecture for computer vision [C]. Proceedings of the IEEE conference on computer vision and pattern recognition. DOI: 10.1109/CVPR.2016.308 . | |
| Tan M, Le Q. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks [C]. International conference on machine learning. PMLR, 6105-6114. DOI: 10.48550/arXiv.1905.11946 . | |
| Villanueva?Rivera L J, Pijanowski B C, Doucette J, Pekin B. 2011. A primer of acoustic analysis for landscape ecologists [J]. Landscape Ecology, 26: 1233-1246. | |
| Wang Z D, Ma H G, Zhong X K, Zhu C Y, Wang Y X Wang J C, Fan P F. 2024. In the songs of Hainan gibbons: automated individual dynamic monitoring from acoustic recordings [J]. Biological Conservation, 294: 110634. DOI: 10.1016/j.biocon.2024.110634 . | |
| Wolfenden A D, Slabbekoorn H, Kluk K, de Kort S R. 2019. Aircraft sound exposure leads to song frequency decline and elevated aggression in wild chiffchaffs [J]. Journal of Animal Ecology, 88 (11): 1720-1731. | |
| Xie J, Colonna J G, Zhang J. 2021. Bioacoustic signal denoising: a review [J]. Artificial Intelligence Review, 54 (5): 3575-3597. | |
| Xie J, Hu K, Zhu M, Guo Y. 2020. Bioacoustic signal classification in continuous recordings: syllable?segmentation vs sliding?window [J]. Expert Systems with Applications, 152: 113390. DOI: 10.1016/j.eswa.2020.113390 . | |
| Xie J, Zhong Y, Zhang J, Liu S, Ding C, Triantafyllopoulos A. 2023. A review of automatic recognition technology for bird vocalizations in the deep learning era [J]. Ecological Informatics, 73:101927.DOI: 10.1016/j.ecoinf.2022.101927 . | |
| Zambolli A H, Manzano M C R, Honda L K, Rezende G C, Culot L. 2023. Performance of autonomous recorders to detect a cryptic and endangered primate species, the black lion?tamarin (Leontopithecus chrysopygus) [J]. American Journal of Primatology, 85 (1): e23454. DOI: 10.1002/ajp.23454 . | |
| Zhang C, He K, Gao X, Guo Y. 2024a. Automatic bioacoustics noise reduction method based on a deep feature loss network [J]. Ecological Informatics, 80: 102517. DOI: 10.1016/j.ecoinf.2024.102517 . | |
| Zhang C, Jin N, Xie J, Hao Z. 2024b. CicadaNet: Deep learning based automatic cicada chorus filtering for improved long?term bird monitoring [J]. Ecological Indicators, 158: 111423. DOI: 10.1016/j.ecolind.2023.111423 . | |
| Zhang C, Zhang Y, Zheng X, Gao X, Hao Z. 2024c. Influence of recording devices and environmental noise on acoustic index scores: Implications for bird sound?based assessments [J]. Ecological Indicators, 159: 111759. DOI: 10.1016/j.ecolind.2024.111759 . | |
| Znidersic E, Towsey M, Roy W K, Darling S E, Truskinger A, Roe P, Watson D M. 2020. Using visualization and machine learning methods to monitor low detectability species: The least bittern as a case study [J]. Ecological Informatics, 55: 101014. DOI: 10.1016/j.ecoinf.2019.101014 . | |
| 杜元宝, 涂炜山, 杨乐, 顾党恩, 郭宝成, 刘宣. 2023. 外来入侵脊椎动物对生物多样性危害的研究进展 [J]. 中国科学: 生命科学, 53 (7): 1035-1054. | |
| 张同作, 江峰, 张婧捷, 蔡振媛, 高红梅, 顾海峰, 宋鹏飞. 2023. 三江源国家公园野生动物保护与管理对策 [J]. 兽类学报, 43 (2): 193-205. |
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