CoChat
Sign Up
Cited by 471Music and Audio ProcessingAnimal Vocal Communication and BehaviorSpeech Recognition and Synthesis

Computational bioacoustics with deep learning: a review and roadmap

Open access
Stowell, Dan·2022·PeerJ
DOI 10.7717/peerj.13152PMC PMC8944344
Open this paper in CoChat — free account

Abstract

Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7717/peerj.13152

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Manually processing the vast amount of data gathered through passive acoustic monitoring can require significant time investment, often not feasible for most researchers.

    “Manually processing the vast amount of data gathered through passive acoustic monitoring can require significant time investment, often not feasible for most researchers (Stowell 2022).”

    From Roars, Rumbles, and Resonance: A Systematic Review and Meta‐Analysis of Crocodylian Acoustic Signals (2026) · cites this paper as Stowell 2022

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 216

  1. Abeßer J A review of deep learning based methods for acoustic scene classification Applied Sciences 2020 10 6 2020 10.3390/app10062020 · DOI
  2. Acconcjaioco M Ntalampiras S One-shot learning for acoustic identification of bird species in non-stationary environments 2021 2020 25th International Conference on Pattern Recognition (ICPR) Piscataway IEEE 755 762
  3. Adavanne S Drossos K Çakr E Virtanen T Stacked convolutional and recurrent neural networks for bird audio detection 2017 Proceedings of EUSIPCO 2017 Special Session on Bird Audio Signal Processing 1729 1733
  4. Adavanne S Politis A Virtanen T Adavanne S Politis A Virtanen T Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network 2018 2018 26th European Signal Processing Conference (EUSIPCO) Piscataway IEEE 1462 1466
  5. Adi K Johnson MT Osiejuk TS Acoustic censusing using automatic vocalization classification and identity recognition Journal of the Acoustical Society of America 2010 127 2 874 883 10.1121/1.3273887 20136210 · DOI
  6. Allen AN Harvey M Harrell L Jansen A Merkens KP Wall CC Cattiau J Oleson EM A convolutional neural network for automated detection of humpback whale song in a diverse, long-term passive acoustic dataset Frontiers in Marine Science 2021 8 165 10.3389/fmars.2021.607321 · DOI
  7. Arteta C Lempitsky V Zisserman A Counting in the wild 2016 European Conference on Computer Vision Berlin Springer 483 498
  8. Baevski A Zhou Y Mohamed A Auli M wav2vec 2.0: a framework for self-supervised learning of speech representations Advances in Neural Information Processing Systems (NeurIPS) 2020 33 12449 12460
  9. Bai S Kolter JZ Koltun V An empirical evaluation of generic convolutional and recurrent networks for sequence modeling 2018 ArXiv preprint arXiv:1803.01271 · arXiv
  10. Bain M Nagrani A Schofield D Berdugo S Bessa J Owen J Hockings KJ Matsuzawa T Hayashi M Biro D Carvalho S Zisserman A Automated audiovisual behavior recognition in wild primates Science Advances 2021 7 46 eabi4883 10.1126/sciadv.abi4883 34767448 PMC8589313 · DOI
  11. Baker E Vincent S A deafening silence: a lack of data and reproducibility in published bioacoustics research? Biodiversity Data Journal 2019 7 e36783 10.3897/BDJ.7.e36783 31723333 PMC6834726 · DOI · Paper page
  12. Balestriero R Cosentino R Glotin H Baraniuk R Spline filters for end-to-end deep learning 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), volume 80 of Proceedings of Machine Learning Research
  13. Baumann J Lohrenz T Roy A Fingscheidt T Beyond the dcase 2017 challenge on rare sound event detection: a proposal for a more realistic training and test framework 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE
  14. Beecher MD Signalling systems for individual recognition: an information theory approach Animal Behaviour 1989 38 2 248 261 10.1016/S0003-3472(89)80087-9 · DOI
  15. Bergler C Schmitt M Cheng RX Schröter H Maier A Barth V Weber M Nöth E Ekštein K Deep representation learning for orca call type classification 2019a Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science 11697 Cham Springer 274 286 10.1007/978-3-030-27947-9_23 · DOI
  16. Bergler C Schröter H Cheng RX Barth V Weber M Nöth E Hofer H Maier A Orca-spot: an automatic killer whale sound detection toolkit using deep learning Scientific Reports 2019b 9 1 1 17 10.1038/s41598-019-47335-w 31358873 PMC6662697 · DOI
  17. Best P Ferrari M Poupard M Paris S Marxer R Symonds H Spong P Glotin H Deep learning and domain transfer for orca vocalization detection 2020 International Joint Conference on Neural Networks
  18. Bhatia R Bird song synthesis using neural vocoders 2021 Master’s thesis, Itä-Suomen yliopisto
  19. Bjorck J Rappazzo BH Chen D Bernstein R Wrege PH Gomes CP Automatic detection and compression for passive acoustic monitoring of the African forest elephant Proceedings of the AAAI Conference on Artificial Intelligence 2019 33 476 484 10.1609/aaai.v33i01.3301476 · DOI
  20. Bravo Sanchez FJ Hossain MR English NB Moore ST Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture Scientific Reports 2021 11 1 1 12 10.1038/s41598-021-95076-6 34344970 PMC8333097 · DOI
  21. Brown A Montgomery J Garg S Automatic construction of accurate bioacoustics workflows under time constraints using a surrogate model Applied Soft Computing 2021 107944 12 107944 10.1016/j.asoc.2021.107944 · DOI
  22. Cakir E Adavanne S Parascandolo G Drossos K Virtanen T Convolutional recurrent neural networks for bird audio detection 2017 Proceedings of EUSIPCO 2017 Piscataway IEEE 1744 1748
  23. Çakır E Parascandolo G Heittola T Huttunen H Virtanen T Convolutional recurrent neural networks for polyphonic sound event detection 2017 ArXiv preprint arXiv:1702.06286 · arXiv
  24. Canziani A Paszke A Culurciello E An analysis of deep neural network models for practical applications 2016 ArXiv preprint arXiv:1605.07678 · arXiv
  25. Chait M How the brain discovers structure in sound sequences Acoustical Science and Technology 2020 41 1 48 53 10.1250/ast.41.48 · DOI
  26. Chen H Xie W Vedaldi A Zisserman A VGGSound: a large-scale audio-visual dataset 2020a Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 721 725
  27. Chen RTQ Amos B Nickel M Neural spatio-temporal point processes 2020b ArXiv preprint arXiv:2011.04583 · arXiv
  28. Chen X Zhao J Chen YH Zhou W Hughes AC Automatic standardized processing and identification of tropical bat calls using deep learning approaches Biological Conservation 2020c 241 2 108269 10.1016/j.biocon.2019.108269 · DOI
  29. Chorowski JK Bahdanau D Serdyuk D Cho K Bengio Y Attention-based models for speech recognition 2015 Advances in Neural Information Processing systems 577 585
  30. Çoban EB Pir D So R Mandel MI Transfer learning from Youtube soundtracks to tag arctic ecoacoustic recordings 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 726 730
  31. Coffey KR Marx RG Neumaier JF DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations Neuropsychopharmacology 2019 44 5 859 868 10.1038/s41386-018-0303-6 30610191 PMC6461910 · DOI
  32. Cohen-Hadria A Cartwright M McFee B Bello JP Voice anonymization in urban sound recordings 2019 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) Piscataway IEEE 1 6
  33. Colonna J Peet T Ferreira CA Jorge AM Gomes EF Gama J Automatic classification of anuran sounds using convolutional neural networks 2016 Proceedings of the Ninth International c* Conference on Computer Science & Software Engineering 73 78
  34. Cramer J Lostanlen V Farnsworth A Salamon J Bello JP Chirping up the right tree: incorporating biological taxonomies into deep bioacoustic classifiers 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 901 905
  35. Dekkers G Rosas F van Waterschoot T Vanrumste B Karsmakers P Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities Information Fusion 2022 77 5 196 210 10.1016/j.inffus.2021.07.022 · DOI
  36. Dias FF Ponti MA Minghim R A classification and quantification approach to generate features in soundscape ecology using neural networks Neural Computing and Applications 2021 34 1923 1937 10.1007/s00521-021-06501-w · DOI
  37. Disabato S Canonaco G Flikkema PG Roveri M Alippi C Birdsong detection at the edge with deep learning 2021 2021 IEEE International Conference on Smart Computing (SMARTCOMP) Piscataway IEEE 9 16
  38. Dufourq E Durbach I Hansford JP Hoepfner A Ma H Bryant JV Stender CS Li W Liu Z Chen Q Zhou Z Turvey ST Pettorelli N Astaras C Automated detection of Hainan Gibbon calls for passive acoustic monitoring Remote Sensing in Ecology and Conservation 2021 7 3 475 487 10.1002/rse2.201 · DOI
  39. Elliott D Otero CE Wyatt S Martino E Tiny transformers for environmental sound classification at the edge 2021 ArXiv preprint arXiv:2103.12157 · arXiv
  40. Fairbrass AJ Firman M Williams C Brostow GJ Titheridge H Jones KE CityNet—deep learning tools for urban ecoacoustic assessment Methods in Ecology and Evolution 2019 10 2 186 197 10.1111/2041-210X.13114 · DOI · Paper page
  41. Fonseca AH Santana GM Ortiz GMB Bampi S Dietrich MO Analysis of ultrasonic vocalizations from mice using computer vision and machine learning Elife 2021 10 e59161 10.7554/eLife.59161 33787490 PMC8057810 · DOI
  42. Fox EJS Call-independent identification in birds 2008 PhD thesis, University of Western Australia, Perth, Australia
  43. Francl A McDermott JH Deep neural network models of sound localization reveal how perception is adapted to real-world environments bioRxiv 2020 10.1101/2020.07.21.214486 PMC8830739 35087192 · DOI
  44. Frazao F Padovese B Kirsebom OS Workshop report: detection and classification in marine bioacoustics with deep learning 2020 ArXiv preprint arXiv:2002.08249 · arXiv
  45. Fujimori K Raytchev B Kaneda K Yamada Y Teshima Y Fujioka E Hiryu S Tamaki T Localization of flying bats from multichannel audio signals by estimating location map with convolutional neural networks Journal of Robotics and Mechatronics 2021 33 3 515 525 10.20965/jrm.2021.p0515 · DOI
  46. Gao R Chen C Al-Halah Z Schissler C Grauman K Visualechoes: spatial image representation learning through echolocation 2020 European Conference on Computer Vision Berlin Springer 658 676
  47. Garcia HA Couture T Galor A Topple JM Huang W Tiwari D Ratilal P Comparing performances of five distinct automatic classifiers for fin whale vocalizations in beamformed spectrograms of coherent hydrophone array Remote Sensing 2020 12 2 326 10.3390/rs12020326 · DOI
  48. Gillings S Scott C Nocturnal flight calling behaviour of thrushes in relation to artificial light at night Ibis 2021 163 4 1379 1393 10.1111/ibi.12955 · DOI
  49. Glotin H Ricard J Balestriero R Fast chirplet transform injects priors in deep learning of animal calls and speech 2017 Proceedings of the 2017 ICLR Workshop
  50. Goëau H Glotin H Vellinga W-P Planqué R Joly A LifeCLEF bird identification task 2016: the arrival of deep learning 2016 Working Notes of CLEF 2016-Conference and Labs of the Evaluation Forum Évora, Portugal 440 449
  51. Goodfellow I Bengio Y Courville A Deep learning 2016 Cambridge MIT Press
  52. Goodfellow I Pouget-Abadie J Mirza M Xu B Warde-Farley D Ozair S Courville A Bengio Y Generative adversarial nets 2014 Advances in Neural Information Processing Systems 2672 2680
  53. Graves A Wayne G Danihelka I Neural turing machines 2014 ArXiv preprint arXiv:1410.5401 · arXiv
  54. Grill T Schlüter J Two convolutional neural networks for bird detection in audio signals 2017 Proceedings of EUSIPCO 2017 Special Session on Bird Audio Signal Processing 1764 1768
  55. Gupta G Kshirsagar M Zhong M Gholami S Ferres JL Comparing recurrent convolutional neural networks for large scale bird species classification Scientific Reports 2021 11 1 1 12 10.1038/s41598-021-96446-w 34429468 PMC8385065 · DOI
  56. Hammer H Chazan SE Goldberger J Gannot S Dynamically localizing multiple speakers based on the time-frequency domain EURASIP Journal on Audio, Speech, and Music Processing 2021 2021 1 276 10.1186/s13636-021-00203-w · DOI
  57. Hardt M Price E Price E Srebro N Lee D Sugiyama M Luxburg U Guyon I Garnett R Equality of opportunity in supervised learning Advances in Neural Information Processing Systems 2016 29 Red Hook Curran Associates, Inc
  58. Hassan N Ramli DA Jaafar H Deep neural network approach to frog species recognition 2017 2017 IEEE 13th International Colloquium on Signal Processing & Its Applications (CSPA) Piscataway IEEE 173 178
  59. Heath BE Sethi SS Orme CDL Ewers RM Picinali L How index selection, compression, and recording schedule impact the description of ecological soundscapes Ecology and Evolution 2021 11 19 13206 13217 10.1002/ece3.8042 34646463 PMC8495811 · DOI
  60. Henderson P Hu J Romoff J Brunskill E Jurafsky D Pineau J Towards the systematic reporting of the energy and carbon footprints of machine learning 2020 ArXiv preprint arXiv:2002.05651 · arXiv
  61. Hershey S Chaudhuri S Ellis DP Gemmeke JF Jansen A Moore RC Plakal M Platt D Saurous RA Seybold B Slaney M Weiss RJ Wilson K CNN architectures for large-scale audio classification 2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 131 135
  62. Heuer S Tafo P Holzmann H Dahlke S New aspects in birdsong recognition utilizing the Gabor transform 2019 Proceedings of ICA 2019 Aachen Universitätsbibliothek der RWTH Aachen 2917 2924
  63. Hibino S Suzuki C Nishino T Classification of singing insect sounds with convolutional neural network Acoustical Science and Technology 2021 42 6 354 356 10.1250/ast.42.354 · DOI
  64. Hill AP Prince P Covarrubias EP Doncaster CP Snaddon JL Rogers A AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment Methods in Ecology and Evolution 2018 9 5 1199 1211 10.1111/2041-210X.12955 · DOI
  65. Himawan I Towsey M Law B Roe P Deep learning techniques for koala activity detection 2018 Proceedings of INTERSPEECH 2018 Baixas International Speech Communication Association 2107 2111
  66. Houégnigan L Safari P Nadeu C van der Schaar M André M A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning 2017 OCEANS 2017-Aberdeen Piscataway IEEE 1 5
  67. Huang G Liu Z Weinberger KQ Densely connected convolutional networks 2016 ArXiv preprint arXiv:1608.06993 · arXiv
  68. Ibrahim AK Zhuang H Chérubin LM Erdol N O’Corry-Crowe G Ali AM A multimodel deep learning algorithm to detect north Atlantic right whale up-calls The Journal of the Acoustical Society of America 2021 150 2 1264 1272 10.1121/10.0005898 34470309 · DOI
  69. Ibrahim AK Zhuang H Chérubin LM Schärer-Umpierre MT Erdol N Automatic classification of grouper species by their sounds using deep neural networks The Journal of the Acoustical Society of America 2018 144 3 EL196 EL202 10.1121/1.5054911 30424627 · DOI
  70. Islam S Valles D Houston toad and other chorusing amphibian species call detection using deep learning architectures 2020 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Piscataway IEEE 511 516
  71. Ivanenko A Watkins P van Gerven MA Hammerschmidt K Englitz B Classifying sex and strain from mouse ultrasonic vocalizations using deep learning PLoS Computational Biology 2020 16 6 e1007918 10.1371/journal.pcbi.1007918 32569292 PMC7347231 · DOI
  72. Jain M Balakrishnan R Microhabitat selection in an assemblage of crickets (orthoptera: Ensifera) of a tropical evergreen forest in southern India Insect Conservation and Diversity 2011 4 2 152 158 10.1111/j.1752-4598.2010.00118.x · DOI
  73. Jancovic P Köküer M Bird species recognition using unsupervised modeling of individual vocalization elements IEEE/ACM Transactions on Audio, Speech, and Language Processing 2019 27 5 932 947 10.1109/TASLP.2019.2904790 · DOI
  74. Janetzky P Davidson P Steininger M Krause A Hotho A Detecting presence of speech in acoustic data obtained from beehives 2021 Proceedings of the 2021 DCASE Workshop
  75. Jansson A Humphrey E Montecchio N Bittner R Kumar A Weyde T Singing voice separation with deep u-net convolutional networks 2017 Proceedings of ISMIR 2017
  76. Jiang JJ Bu LR Duan FJ Wang XQ Liu W Sun ZB Li CY Whistle detection and classification for whales based on convolutional neural networks Applied Acoustics 2019 150 1–4 169 178 10.1016/j.apacoust.2019.02.007 · DOI
  77. Johnston A Newson SE Risely K Musgrove AJ Massimino D Baillie SR Pearce-Higgins JW Species traits explain variation in detectability of UK birds Bird Study 2014 61 3 340 350 10.1080/00063657.2014.941787 · DOI
  78. Jolles JW Broad-scale applications of the Raspberry Pi: a review and guide for biologists Methods in Ecology and Evolution 2021 12 9 1562 1579 10.1111/2041-210X.13652 · DOI
  79. Joly A Goëau H Glotin H Spampinato C Bonnet P Vellinga W-P Lombardo J-C Planqué R Palazzo S Müller H Biodiversity information retrieval through large scale content-based identification: a long-term evaluation 2019 Information Retrieval Evaluation in a Changing World Berlin Springer 389 413
  80. Joly A Goëau H Kahl S Picek L Lorieul T Cole E Deneu B Servajean M Durso A Bolon I Glotin H Planqué R de Castañeda RR Vellinga WP Klinck H Denton T Eggel I Bonnet P Müller H Overview of lifeclef 2021: an evaluation of machine-learning based species identification and species distribution prediction 2021 International Conference of the Cross-Language Evaluation Forum for European Languages Berlin Springer 371 393
  81. Jones DL Baraniuk RG An adaptive optimal-kernel time-frequency representation IEEE Transactions on Signal Processing 1995 43 10 2361 2371 10.1109/78.469854 · DOI
  82. Jung D-H Kim NY Moon SH Jhin C Kim H-J Yang J-S Kim HS Lee TS Lee JY Park SH Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering Animals 2021 11 2 357 10.3390/ani11020357 33535390 PMC7911430 · DOI
  83. Khalighifar A Jiménez-Garca D Campbell LP Ahadji-Dabla KM Aboagye-Antwi F Ibarra-Juárez LA Peterson AT Application of deep learning to community-science-based mosquito monitoring and detection of novel species Journal of Medical Entomology 2021 59 1 355 362 10.1093/jme/tjab161 34546359 · DOI
  84. Kholghi M Phillips Y Towsey M Sitbon L Roe P Active learning for classifying long-duration audio recordings of the environment Methods in Ecology and Evolution 2018 9 9 1948 1958 10.1111/2041-210X.13042 · DOI
  85. Kiskin I Sinka M Cobb AD Rafique W Wang L Zilli D Gutteridge B Dam R Marinos T Li Y Msaky D Kaindoa E Killeen G Herreros-Moya E Willis KJ Roberts SJ HumBugDB: a large-scale acoustic mosquito dataset 2021 ArXiv preprint arXiv:2110.07607 · arXiv
  86. Kiskin I Zilli D Li Y Sinka M Willis K Roberts S Bioacoustic detection with wavelet-conditioned convolutional neural networks Neural Computing and Applications 2020 32 4 915 927 10.1007/s00521-018-3626-7 · DOI
  87. Kitzes J Schricker L The necessity, promise and challenge of automated biodiversity surveys Environmental Conservation 2019 46 4 247 250 10.1017/S0376892919000146 · DOI
  88. Knight E Hannah K Foley G Scott C Brigham R Bayne E Recommendations for acoustic recognizer performance assessment with application to five common automated signal recognition programs Avian Conservation and Ecology 2017 12 2 14 10.5751/ACE-01114-120214 · DOI
  89. Knight EC Poo Hernandez S Bayne EM Bulitko V Tucker BV Pre-processing spectrogram parameters improve the accuracy of bioacoustic classification using convolutional neural networks Bioacoustics-the International Journal of Animal Sound and Its Recording 2020 29 3 337 355 10.1080/09524622.2019.1606734 · DOI · Paper page
  90. Kobayashi K Masuda K Haga C Matsui T Fukui D Machimura T Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks Ecological Informatics 2021 62 101253 10.1016/j.ecoinf.2021.101253 · DOI
  91. Kojima R Sugiyama O Hoshiba K Suzuki R Nakadai K Hark-bird-box: a portable real-time bird song scene analysis system 2018 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Piscataway IEEE 2497 2502
  92. Kong Q Xu Y Plumbley M Joint detection and classification convolutional neural network on weakly labelled bird audio detection 2017 Proceedings of EUSIPCO 2017 Special Session on Bird Audio Signal Processing 1749 1753
  93. Koops HV Van Balen J Wiering F Automatic segmentation and deep learning of bird sounds 2015 International Conference of the Cross-Language Evaluation Forum for European Languages Berlin Springer 261 267
  94. Koumura T Okanoya K Automatic recognition of element classes and boundaries in the birdsong with variable sequences PLOS ONE 2016 11 7 e0159188 10.1371/journal.pone.0159188 27442240 PMC4956110 · DOI
  95. Laiolo P The emerging significance of bioacoustics in animal species conservation Biological Conservation 2010 143 7 1635 1645 10.1016/j.biocon.2010.03.025 · DOI
  96. LeBien J Zhong M Campos-Cerqueira M Velev JP Dodhia R Ferres JL Aide TM A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network Ecological Informatics 2020 59 5 101113 10.1016/j.ecoinf.2020.101113 · DOI
  97. LeCun Y Bengio Y Hinton G Deep learning Nature 2015 521 7553 436 444 10.1038/nature14539 26017442 · DOI · Paper page
  98. LeCun Y Bottou L Bengio Y Haffner P Gradient-based learning applied to document recognition Proceedings of the IEEE 1998 86 11 2278 2324 10.1109/5.726791 · DOI · Paper page
  99. Li L Qiao G Liu S Qing X Zhang H Mazhar S Niu F Automated classification of Tursiops aduncus whistles based on a depth-wise separable convolutional neural network and data augmentation The Journal of the Acoustical Society of America 2021 150 5 3861 3873 10.1121/10.0007291 34852567 · DOI
  100. Li P Liu X Palmer K Fleishman E Gillespie D Nosal E-M Shiu Y Klinck H Cholewiak D Helble T Roch MA Learning deep models from synthetic data for extracting dolphin whistle contours 2020 2020 International Joint Conference on Neural Networks (IJCNN) Piscataway IEEE 1 10
  101. Li S Li X Xing Z Zhang Z Wang Y Li R Guo R Xie J Intelligent audio bird repeller for transmission line tower based on bird species variation 2019 IOP Conference Series: Materials Science and Engineering 592 Bristo IOP Publishing 12142
  102. Lin T-H Tsao Y Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval Remote Sensing in Ecology and Conservation 2020 6 3 236 247 10.1002/rse2.141 · DOI
  103. Linhart P Osiejuk T Budka M Šálek M Špinka M Policht R Syrová M Blumstein DT Measuring individual identity information in animal signals: overview and performance of available identity metrics Methods in Ecology and Evolution 2019 10 9 1558 1570 10.1111/2041-210X.13238 · DOI
  104. Linke S Gifford T Desjonquères C Tonolla D Aubin T Barclay L Karaconstantis C Kennard MJ Rybak F Sueur J Freshwater ecoacoustics as a tool for continuous ecosystem monitoring Frontiers in Ecology and the Environment 2018 16 4 231 238 10.1002/fee.1779 · DOI
  105. Lostanlen V Arnaud P du Gardin M Godet L Lagrange M An interactive human—animal—robot approach to distance sampling in bioacoustics 2021a Proceedings of the 3rd International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots (VIHAR 2021)
  106. Lostanlen V Bernabeu A Béchennec JL Briday M Faucou S Lagrange M Energy efficiency is not enough: towards a batteryless internet of sounds 2021b International Workshop on the Internet of Sounds New York Association for Computing Machinery
  107. Lostanlen V Palmer K Knight E Clark C Klinck H Farnsworth A Wong T Cramer J Bello JP Long-distance detection of bioacoustic events with per-channel energy normalization 2019a Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019) 144 148
  108. Lostanlen V Salamon J Farnsworth A Kelling S Bello JP Birdvox-full-night: a dataset and benchmark for avian flight call detection 2018 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 266 270
  109. Lostanlen V Salamon J Farnsworth A Kelling S Bello JP Robust sound event detection in bioacoustic sensor networks PLOS ONE 2019b 14 10 1 31 10.1371/journal.pone.0214168 PMC6812790 31647815 · DOI
  110. Madhusudhana S Shiu Y Klinck H Fleishman E Liu X Nosal E-M Helble T Cholewiak D Gillespie D Širović A Roch MA Improve automatic detection of animal call sequences with temporal context Journal of the Royal Society Interface 2021 18 180 20210297 10.1098/rsif.2021.0297 PMC8292017 34283944 · DOI
  111. Maegawa Y Ushigome Y Suzuki M Taguchi K Kobayashi K Haga C Matsui T A new survey method using convolutional neural networks for automatic classification of bird calls Ecological Informatics 2021 61 4 101164 10.1016/j.ecoinf.2020.101164 · DOI
  112. Marchal J Fabianek F Aubry Y Software performance for the automated identification of bird vocalisations: the case of two closely related species Bioacoustics-the International Journal of Animal Sound and Its Recording 2021 68 3 1 17 10.1080/09524622.2021.1945952 · DOI
  113. Marler PR Slabbekoorn H Nature’s music: the science of birdsong 2004 Cambridge Academic Press
  114. Mercado E III Sturdy CB Brown CH Riede T Chapter 10: classifying animal sounds with neural networks Comparative Bioacoustics: An Overview 2017 Oak Park Bentham Science Publishers 415 461
  115. Mesaros A Diment A Elizalde B Heittola T Vincent E Raj B Virtanen T Sound event detection in the DCASE 2017 challenge IEEE/ACM Transactions on Audio, Speech and Language Processing 2019 27 6 992 1006 10.1109/TASLP.2019.2907016 · DOI
  116. Mesaros A Heittola T Virtanen T Metrics for polyphonic sound event detection Applied Sciences 2016 6 6 162 10.3390/app6060162 · DOI
  117. Mesaros A Heittola T Virtanen T Plumbley MD Sound event detection: a tutorial IEEE Signal Processing Magazine 2021 38 5 67 83 10.1109/MSP.2021.3090678 · DOI
  118. Mishachandar B Vairamuthu S Diverse ocean noise classification using deep learning Applied Acoustics 2021 181 108141 10.1016/j.apacoust.2021.108141 · DOI
  119. Montgomery GA Dunn RR Fox R Jongejans E Leather SR Saunders ME Shortall CR Tingley MW Wagner DL Is the insect apocalypse upon us? How to find out Biological Conservation 2020 241 3 108327 10.1016/j.biocon.2019.108327 · DOI
  120. Morfi V Bas Y Pamuła H Glotin H Stowell D Nips4bplus: a richly annotated birdsong audio dataset PeerJ Computer Science 2019 5 6 e223 10.7717/peerj-cs.223 PMC7924541 33816876 · DOI
  121. Morfi V Lachlan RF Stowell D Deep perceptual embeddings for unlabelled animal sound events The Journal of the Acoustical Society of America 2021a 150 1 2 11 10.1121/10.0005475 34340499 · DOI
  122. Morfi V Nolasco I Lostanlen V Singh S Strandburg-Peshkin A Gill L Pamuła H Benvent D Stowell D Few-shot bioacoustic event detection: a new task at the DCASE, 2021 challenge 2021b Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) Barcelona, Spain 145 149
  123. Morfi V Stowell D Deep learning for audio event detection and tagging on low-resource datasets Applied Sciences 2018 8 8 1397 10.3390/app8081397 · DOI
  124. Morgan M Braasch J Long-term deep learning-facilitated environmental acoustic monitoring in the capital region of New York State Ecological Informatics 2021 61 101242 10.1016/j.ecoinf.2021.101242 · DOI
  125. Nanni L Rigo A Lumini A Brahnam S Spectrogram classification using dissimilarity space Applied Sciences 2020 10 12 4176 10.3390/app10124176 · DOI
  126. Narasimhan R Fern XZ Raich R Simultaneous segmentation and classification of bird song using CNN 2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 146 150
  127. Niculescu-Mizil A Caruana R Predicting good probabilities with supervised learning 2005 Proceedings of the 22nd International Conference on Machine Learning New York ACM 625 632
  128. Nolasco I Stowell D Rank-based loss for learning hierarchical representations 2022 ArXiv preprint arXiv:2110.05941 · arXiv
  129. Ntalampiras S Bird species identification via transfer learning from music genres Ecological Informatics 2018 44 12 76 81 10.1016/j.ecoinf.2018.01.006 · DOI
  130. Ntalampiras S Potamitis I Acoustic detection of unknown bird species and individuals CAAI Transactions on Intelligence Technology 2021 6 3 291 300 10.1049/cit2.12007 · DOI
  131. Oikarinen T Srinivasan K Meisner O Hyman JB Parmar S Fanucci-Kiss A Desimone R Landman R Feng G Deep convolutional network for animal sound classification and source attribution using dual audio recordings The Journal of the Acoustical Society of America 2019 145 2 654 662 10.1121/1.5087827 30823820 PMC6786887 · DOI
  132. Ozanich E Thode A Gerstoft P Freeman LA Freeman S Deep embedded clustering of coral reef bioacoustics The Journal of the Acoustical Society of America 2021 149 4 2587 2601 10.1121/10.0004221 33940892 · DOI
  133. Padovese B Frazao F Kirsebom OS Matwin S Data augmentation for the classification of North Atlantic right whales upcalls The Journal of the Acoustical Society of America 2021 149 4 2520 2530 10.1121/10.0004258 33940913 · DOI
  134. Phillips YF Towsey M Roe P Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation PLOS ONE 2018 13 3 1 27 10.1371/journal.pone.0193345 PMC5832236 29494629 · DOI
  135. Pons J Serrà J Serra X Training neural audio classifiers with few data 2019 https://arxiv.org/abs/1810.10274 · arXiv
  136. Premoli M Baggi D Bianchetti M Gnutti A Bondaschi M Mastinu A Migliorati P Signoroni A Leonardi R Memo M Bonini SA Automatic classification of mice vocalizations using machine learning techniques and convolutional neural networks PLOS ONE 2021 16 11 e0244636 10.1371/journal.pone.0244636 33465075 PMC7815145 · DOI
  137. Prince P Hill A Piña Covarrubias E Doncaster P Snaddon JL Rogers A Deploying acoustic detection algorithms on low-cost, open-source acoustic sensors for environmental monitoring Sensors 2019 19 3 553 10.3390/s19030553 PMC6387379 30699950 · DOI
  138. Ptacek L Machlica L Linhart P Jaska P Muller L Automatic recognition of bird individuals on an open set using as-is recordings Bioacoustics-the International Journal of Animal Sound and Its Recording 2016 25 1 55 73 10.1080/09524622.2015.1089524 · DOI
  139. Qian K Zhang Z Baird A Schuller B Active learning for bird sound classification via a kernel-based extreme learning machine The Journal of the Acoustical Society of America 2017 142 4 1796 1804 10.1121/1.5004570 29092546 · DOI
  140. Ranft R Natural sound archives: past, present and future Anais da Academia Brasileira de Ciências 2004 76 2 456 460 10.1590/S0001-37652004000200041 15258666 · DOI
  141. Ravanelli M Bengio Y Speech and speaker recognition from raw waveform with SincNet 2018 ArXiv preprint arXiv:1812.05920 · arXiv
  142. Ren Z Kong Q Qian K Plumbley MD Schuller B Attention-based convolutional neural networks for acoustic scene classification 2018 Proceedings of DCASE 2018
  143. Rigakis I Potamitis I Tatlas N-A Potirakis SM Ntalampiras S TreeVibes: modern tools for global monitoring of trees for borers Smart Cities 2021 4 1 271 285 10.3390/smartcities4010017 · DOI
  144. Roch MA Lindeneau S Aurora GS Frasier KE Hildebrand JA Glotin H Baumann-Pickering S Using context to train time-domain echolocation click detectors The Journal of the Acoustical Society of America 2021 149 5 3301 3310 10.1121/10.0004992 34241092 · DOI
  145. Roch MA Miller P Helble TA Baumann-Pickering S Širović A Organizing metadata from passive acoustic localizations of marine animals The Journal of the Acoustical Society of America 2017 141 5 3605 10.1121/1.4987714 · DOI
  146. Roe P Eichinski P Fuller RA McDonald PG Schwarzkopf L Towsey M Truskinger A Tucker D Watson DM The Australian Acoustic Observatory Methods in Ecology and Evolution 2021 12 10 1802 1808 10.1111/2041-210X.13660 · DOI
  147. Romero-Mujalli D Bergmann T Zimmermann A Scheumann M Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations Scientific Reports 2021 11 1 1 15 10.1038/s41598-021-03941-1 34961788 PMC8712519 · DOI
  148. Rowe B Eichinski P Zhang J Roe P Acoustic auto-encoders for biodiversity assessment Ecological Informatics 2021 62 1 101237 10.1016/j.ecoinf.2021.101237 · DOI
  149. Ruff ZJ Lesmeister DB Appel CL Sullivan CM Workflow and convolutional neural network for automated identification of animal sounds Ecological Indicators 2021 124 2 107419 10.1016/j.ecolind.2021.107419 · DOI · Paper page
  150. Rumelt RB Basto A Roncal CM Automated audio recording as a means of surveying tinamous (Tinamidae) in the Peruvian Amazon Ecology and Evolution 2021 11 19 13518 13531 10.1002/ece3.8078 34646487 PMC8495786 · DOI
  151. Ryazanov I Nylund AT Basu D Hassellöv I-M Schliep A Deep learning for deep waters: an expert-in-the-loop machine learning framework for marine sciences Journal of Marine Science and Engineering 2021 9 2 169 10.3390/jmse9020169 · DOI
  152. Saeed A Grangier D Zeghidour N Contrastive learning of general-purpose audio representations 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE
  153. Salamon J Bello JP Farnsworth A Kelling S Fusing shallow and deep learning for bioacoustic bird species classification 2017a 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 141 145
  154. Salamon J MacConnell D Cartwright M Li P Bello JP Scaper: a library for soundscape synthesis and augmentation 2017b 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Piscataway IEEE 344 348
  155. Sandler M Howard A Zhu M Zhmoginov A Chen L-C Mobilenetv2: inverted residuals and linear bottlenecks 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Piscataway IEEE 4510 4520
  156. Schröter H Nöth E Maier A Cheng R Barth V Bergler C Segmentation, classification, and visualization of orca calls using deep learning 2019 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 8231 8235
  157. Searby A Jouventin P Aubin T Acoustic recognition in macaroni penguins: an original signature system Animal Behaviour 2004 67 4 615 625 10.1016/j.anbehav.2003.03.012 · DOI
  158. Sethi SS Jones NS Fulcher BD Picinali L Clink DJ Klinck H Orme CDL Wrege PH Ewers RM Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set Proceedings of The National Academy of Sciences of The United States of America 2020 117 29 17049 17055 10.1073/pnas.2004702117 32636258 PMC7382238 · DOI
  159. Shimada K Koyama Y Takahashi N Takahashi S Mitsufuji Y Accdoa: activity-coupled cartesian direction of arrival representation for sound event localization and detection 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 915 919
  160. Shiu Y Palmer KJ Roch MA Fleishman E Liu X Nosal E-M Helble T Cholewiak D Gillespie D Klinck H Deep neural networks for automated detection of marine mammal species Scientific Reports 2020 10 1 607 10.1038/s41598-020-57549-y 31953462 PMC6969184 · DOI
  161. Shrestha R Glackin C Wall J Cannings N Bird audio diarization with faster R-CNN 2021 International Conference on Artificial Neural Networks Berlin Springer 415 426
  162. Simon R Bakunowski K Reyes-Vasques A Tschapka M Knörnschild M Steckel J Stowell D Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks PLoS Computational Biology 2021 17 12 e1009706 10.1371/journal.pcbi.1009706 34914700 PMC8718002 · DOI
  163. Simon R Varkevisser J Mendoza E Hochradel K Scharff C Riebel K Halfwerk W Development and application of a robotic zebra finch (RoboFinch) to study multimodal cues in vocal communication PeerJ Preprints 2019 7 e28004v2 10.7287/peerj.preprints.28004v3 · DOI
  164. Simonyan K Zisserman A Very deep convolutional networks for large-scale image recognition 2014 ArXiv preprint arXiv:1409.1556 · arXiv
  165. Sinha R Rajan P A deep autoencoder approach to bird call enhancement 2018 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Piscataway IEEE 22 26
  166. Sinka ME Zilli D Li Y Kiskin I Kirkham D Rafique W Wang L Chan H Gutteridge B Herreros-Moya E Portwood H Roberts S Willis KJ HumBug-an acoustic mosquito monitoring tool for use on budget smartphones Methods in Ecology and Evolution 2021 12 10 1848 1859 10.1111/2041-210X.13663 · DOI
  167. Slonina Z Bonzini AA Brown J Wang S Farkhatdinov I Althoefer K Jamone L Versace E Using robochick to identify the behavioral features promoting social interactions 2021 2021 IEEE International Conference on Development and Learning (ICDL) Piscataway IEEE 1 6
  168. Smith AA Kristensen D Deep learning to extract laboratory mouse ultrasonic vocalizations from scalograms 2017 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Piscataway IEEE 1972 1979
  169. Steinfath E Palacios-Muñoz A Rottschäfer JR Yuezak D Clemens J Fast and accurate annotation of acoustic signals with deep neural networks eLife 2021 10 e68837 10.7554/eLife.68837 34723794 PMC8560090 · DOI
  170. Stevenson BC Borchers DL Altwegg R Swift RJ Gillespie DM Measey GJ A general framework for animal density estimation from acoustic detections across a fixed microphone array Methods in Ecology and Evolution 2015 6 1 38 48 10.1111/2041-210X.12291 · DOI
  171. Stowell D Chapter 11: computational bioacoustic scene analysis 2018 Computational Analysis of Sound Scenes and Events Berlin Springer 303 333
  172. Stowell D Clayton D Acoustic event detection for multiple overlapping similar sources 2015 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Piscataway IEEE
  173. Stowell D Gill LF Clayton D Detailed temporal structure of communication networks in groups of songbirds Journal of the Royal Society Interface 2016a 13 119 20160296 10.1098/rsif.2016.0296 PMC4938092 27335223 · DOI
  174. Stowell D Petrusková T Šálek M Linhart P Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions Journal of The Royal Society Interface 2019a 16 153 20180940 10.1098/rsif.2018.0940 PMC6505557 30966953 · DOI
  175. Stowell D Stylianou Y Wood M Pamuła H Glotin H Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge Methods in Ecology and Evolution 2019b 16 153 368 380 10.1111/2041-210X.13103 · DOI · Paper page
  176. Stowell D Wood M Stylianou Y Glotin H Bird detection in audio: a survey and a challenge 2016b 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) Piscataway IEEE
  177. Szegedy C Liu W Jia Y Sermanet P Reed S Anguelov D Erhan D Vanhoucke V Rabinovich A Going deeper with convolutions 2014 ArXiv preprint arXiv:1409.4842 · arXiv
  178. Teşileanu T Ölveczky B Balasubramanian V Rules and mechanisms for efficient two-stage learning in neural circuits eLife 2017 6 e20944 10.7554/eLife.20944 28374674 PMC5380437 · DOI
  179. Thakur A Thapar D Rajan P Nigam A Deep metric learning for bioacoustic classification: overcoming training data scarcity using dynamic triplet loss The Journal of the Acoustical Society of America 2019 146 1 534 547 10.1121/1.5118245 31370640 · DOI
  180. Thomas M Martin B Kowarski K Gaudet B Matwin S Marine mammal species classification using convolutional neural networks and a novel acoustic representation 2019 Proceedings of the ECMLPKDD 2019
  181. Towsey M Planitz B Nantes A Wimmer J Roe P A toolbox for animal call recognition Bioacoustics 2012 21 2 107 125 10.1080/09524622.2011.648753 · DOI
  182. Traer J McDermott JH Statistics of natural reverberation enable perceptual separation of sound and space Proceedings of The National Academy of Sciences of The United States of America 2016 113 48 E7856 E7865 10.1073/pnas.1612524113 27834730 PMC5137703 · DOI
  183. Turpault N Serizel R Wisdom S Erdogan H Hershey JR Fonseca E Seetharaman P Salamon J Sound event detection and separation: a benchmark on DESED synthetic soundscapes 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Piscataway IEEE 840 844
  184. Tzirakis P Shiarella A Ewers R Schuller BW Computer audition for continuous rainforest occupancy monitoring: the case of Bornean Gibbons’ call detection 2020 Proceedings of InterSpeech 1211 1215
  185. Van Komen DF Neilsen TB Howarth K Knobles DP Dahl PH Seabed and range estimation of impulsive time series using a convolutional neural network The Journal of the Acoustical Society of America 2020 147 5 EL403 EL408 10.1121/10.0001216 32486785 · DOI
  186. Vaswani A Shazeer N Parmar N Uszkoreit J Jones L Gomez AN Kaiser Ł Polosukhin I Attention is all you need 2017 Advances in Neural Information Processing Systems 5998 6008
  187. Venkatesh S Moffat D Miranda ER You only hear once: a YOLO-like algorithm for audio segmentation and sound event detection 2021 ArXiv preprint arXiv:2109.00962 · arXiv
  188. Vesperini F Gabrielli L Principi E Squartini S A capsule neural networks based approach for bird audio detection 2018 Proceedings of the International Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018
  189. Vickers W Milner B Gorpincenko A Lee R Methods to improve the robustness of right whale detection using CNNs in changing conditions 2021a Proceedings of EUSIPCO 2020 European Association for Signal Processing 106 110
  190. Vickers W Milner B Risch D Lee R Robust north Atlantic right whale detection using deep learning models for denoising The Journal of the Acoustical Society of America 2021b 149 6 3797 3812 10.1121/10.0005128 34241455 · DOI
  191. Vidaña-Vila E Navarro J Borda-Fortuny C Stowell D Alsina-Pagès RM Low-cost distributed acoustic sensor network for real-time urban sound monitoring Electronics 2020 9 12 2119 10.3390/electronics9122119 · DOI
  192. Vignal C Mathevon N Mottin S Mate recognition by female zebra finch: analysis of individuality in male call and first investigations on female decoding process Behavioural Processes 2008 77 2 191 198 10.1016/j.beproc.2007.09.003 17980974 · DOI
  193. Waddell EE Rasmussen JH Širović A Applying artificial intelligence methods to detect and classify fish calls from the northern gulf of Mexico Journal of Marine Science and Engineering 2021 9 10 1128 10.3390/jmse9101128 · DOI
  194. Wang K Wu P Cui H Xuan C Su H Identification and classification for sheep foraging behavior based on acoustic signal and deep learning Computers and Electronics in Agriculture 2021 187 195 106275 10.1016/j.compag.2021.106275 · DOI
  195. Webster MS Budney GF Brown CH Riede T Chapter 11: sound archives and media specimens in the 21st century Comparative Bioacoustics: An Overview 2017 Oak Park Bentham Science Publishers
  196. Wolters P Daw C Hutchinson B Phillips L Proposal-based few-shot sound event detection for speech and environmental sounds with perceivers 2021 http://arxiv.org/abs/2107.13616 · arXiv
  197. Wood CM Kahl S Chaon P Peery MZ Klinck H Survey coverage, recording duration and community composition affect observed species richness in passive acoustic surveys Methods in Ecology and Evolution 2021 12 5 885 896 10.1111/2041-210X.13571 · DOI
  198. Xian Y Pu Y Gan Z Lu L Thompson A Modified DCTNet for audio signals classification Acoustical Society of America Journal 2016 140 4 3405 10.1121/1.4970932 · DOI
  199. Xiao S Yan J Farajtabar M Song L Yang X Zha H Learning time series associated event sequences with recurrent point process networks IEEE Transactions on Neural Networks and Learning Systems 2019 30 10 3124 3136 10.1109/TNNLS.2018.2889776 30676979 · DOI
  200. Xie J Colonna JG Zhang J Bioacoustic signal denoising: a review Artificial Intelligence Review 2021a 54 5 3575 3597 10.1007/s10462-020-09932-4 · DOI

200 of 216 shown

Cited by

Sorted by citations

Authors
Stowell, D.
Affiliations
Naturalis Biodiversity Center (NL)
Corresponding author
Stowell, Dan
ORCID
Stowell, Dan
Venue
PeerJ
Publisher
PeerJ
Published
2022-03-21
Volume
10
Pages
e13152
ISSN
2167-8359 (ISSN-L)
DOI
10.7717/peerj.13152
PMC
PMC8944344
OpenAlex
W4226051760
Cited by
471
References
16
Citations by year
2026: 153 · 2025: 129 · 2024: 90 · 2023: 69 · 2022: 30
FWCI
10.22
Type
journal article
Language
en
Open access
Licence
Locations
  • PeerJ (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • Research portal (Tilburg University) (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
deep learning, animal sounds, natural soundscapes, ecoacoustics, acoustic signal processing, animal behavior, ecological monitoring, acoustic signal analysis, research roadmap
MeSH terms
Deep Learning, Acoustics, Animals, Signal Processing, Computer-Assisted, Vocalization, Animal, Ecosystem

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review