Artificial Intelligence Review

, Volume 49, Issue 3, pp 375–391 | Cite as

Frog call classification: a survey

  • Jie Xie
  • Michael Towsey
  • Jinglan Zhang
  • Paul Roe


Over the past decade, frog biodiversity has rapidly declined due to many problems including habitat loss and degradation, introduced invasive species, and environmental pollution. Frogs are greatly important to improve the global ecosystem and it is ever more necessary to monitor frog biodiversity. One way to monitor frog biodiversity is to record audio of frog calls. Various methods have been developed to classify these calls. However, to the best of our knowledge, there is still no paper that reviews and summarizes currently developed methods. This survey gives a quantitative and detailed analysis of frog call classification. To be specific, a frog call classification system consists of signal pre-processing, feature extraction, and classification. Signal pre-processing is made up of signal processing, noise reduction, and syllable segmentation. Following signal preprocessing, the next step is feature extraction, which is the most crucial step for improving classification performance. Features used for frog call classification are categorized into four types: (1) time domain and frequency domain features (we classify time domain and frequency domain features into one type because they are often combined together to achieve higher classification accuracy), (2) time-frequency features, (3) cepstral features, and (4) other features. For the classification step, different classifiers and evaluation criteria used for frog call classification are investigated. In conclusion, we discuss future work for frog call classification.


Frog call classification Bioacoustics Soundscape ecology Sensor ecology Acoustic ecology 


  1. Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, Villanueva-Rivera LJ, Aide TM (2009) Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecol Inform 4(4):206–214CrossRefGoogle Scholar
  2. Bedoya C, Isaza C, Daza JM, López JD (2014) Automatic recognition of anuran species based on syllable identification. Ecol Inform 24:200–209CrossRefGoogle Scholar
  3. Brandes TS (2008) Feature vector selection and use with hidden markov models to identify frequency-modulated bioacoustic signals amidst noise. IEEE Trans Audio Speech Lang Process 16(6):1173–1180CrossRefGoogle Scholar
  4. Brandes TS, Naskrecki P, Figueroa HK (2006) Using image processing to detect and classify narrow-band cricket and frog calls. J Acoust Soc Am 120(5):2950–2957CrossRefGoogle Scholar
  5. Camacho A, García-Rodríguez A, Bolaños F (2011) Automatic detection of vocalizations of the frog Diasporus hylaeformis in audio recordings. In: Proceedings of meetings on Acoustics, Acoustical Society of America, vol 14, p 010003Google Scholar
  6. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  7. Chen WP, Chen SS, Lin CC, Chen YZ, Lin WC (2012) Automatic recognition of frog calls using a multi-stage average spectrum. Comput Math Appl 64(5):1270–1281CrossRefGoogle Scholar
  8. Colombia C, del Cauca V (2009) Frogs species classification using lpc and classification algorithms on wireless sensor network platform. In: International science and technology conferenceGoogle Scholar
  9. Colonna JG, Ribas AD, dos Santos EM, Nakamura EF (2012) Feature subset selection for automatically classifying anuran calls using sensor networks. In: The 2012 international joint conference on neural networks (IJCNN). IEEE, pp 1–8Google Scholar
  10. Colonna JG, Cristo M, Junior MS, Nakamura EF (2015) An incremental technique for real-time bioacoustic signal segmentation. Expert Syst Appl 42(21):7367–7374CrossRefGoogle Scholar
  11. Croker B, Kottege N (2012) Using feature vectors to detect frog calls in wireless sensor networks. J Acoust Soc Am 131(5):EL400–EL405CrossRefGoogle Scholar
  12. Dang T, Bulusu N, Hu W (2008) Lightweight acoustic classification for cane-toad monitoring. In: 2008 42nd Asilomar conference on signals, systems and computers. IEEE, pp 1601–1605Google Scholar
  13. Dayou J, Han NC, Mun HC, Ahmad AH, Muniandy SV, Dalimin MN (2011) Classification and identification of frog sound based on entropy approach. In: International conference on life science and technology, vol 3, pp 184–187Google Scholar
  14. Dennis J, Tran HD, Li H (2011) Spectrogram image feature for sound event classification in mismatched conditions. IEEE Signal Process Lett 18(2):130–133CrossRefGoogle Scholar
  15. Dudgeon D, Arthington AH, Gessner MO, Kawabata ZI, Knowler DJ, Lévêque C, Naiman RJ, Prieur-Richard AH, Soto D, Stiassny ML et al (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev 81(2):163–182CrossRefGoogle Scholar
  16. Duellman WE, Trueb L (1994) Biology of amphibians. JHU Press, BaltimoreGoogle Scholar
  17. Fox EJ (2008) A new perspective on acoustic individual recognition in animals with limited call sharing or changing repertoires. Anim Behav 75(3):1187–1194CrossRefGoogle Scholar
  18. Gingras B, Fitch WT (2013) A three-parameter model for classifying anurans into four genera based on advertisement calls. J Acoust Soc Am 133(1):547–559CrossRefGoogle Scholar
  19. Gordon L, Chervonenkis AY, Gammerman AJ, Shahmuradov IA, Solovyev VV (2003) Sequence alignment kernel for recognition of promoter regions. Bioinformatics 19(15):1964–1971CrossRefGoogle Scholar
  20. Grigg G, Taylor A, Mc Callum H, Watson G (1996) Monitoring frog communities: an application of machine learning. In: Proceedings of eighth innovative applications of artificial intelligence conference, Portland, OR, pp 1564–1569Google Scholar
  21. Han NC, Muniandy SV, Dayou J (2011) Acoustic classification of australian anurans based on hybrid spectral-entropy approach. Appl Acoust 72(9):639–645CrossRefGoogle Scholar
  22. Härmä A (2003) Automatic identification of bird species based on sinusoidal modeling of syllables. In: 2003 IEEE international conference on acoustics, speech, and signal processing. Proceedings (ICASSP’03). IEEE, vol 5, pp V–545Google Scholar
  23. Heyer R, Donnelly MA, Foster M, Mcdiarmid R (2014) Measuring and monitoring biological diversity: standard methods for amphibians. Smithsonian Institution, WashingtonGoogle Scholar
  24. Huang CJ, Yang YJ, Yang DX, Chen YJ, Wei HY (2008) Realization of an intelligent frog call identification agent. In: Nguyen NT et al (eds) Agent and multi-agent systems: technologies and applications. Springer, Berlin, pp 93–102CrossRefGoogle Scholar
  25. Huang CJ, Yang YJ, Yang DX, Chen YJ (2009) Frog classification using machine learning techniques. Expert Syst Appl 36(2):3737–3743CrossRefGoogle Scholar
  26. Huang CJ, Chen YJ, Chen HM, Jian JJ, Tseng SC, Yang YJ, Hsu PA (2014) Intelligent feature extraction and classification of anuran vocalizations. Appl Soft Comput 19:1–7CrossRefGoogle Scholar
  27. Jaafar H, Ramli D (2013) Automatic syllables segmentation for frog identification system. In: 2013 IEEE 9th international colloquium on signal processing and its applications (CSPA), pp 224–228Google Scholar
  28. Jaafar H, Ramli DA, Shahrudin S (2013a) A comparative study of classification algorithms and feature extractions for frog identification system. School of Electrical and Electronic 4th Postgraduate Colloquium, Perak, Malaysia 4Google Scholar
  29. Jaafar H, Ramli DA, Shahrudin S (2013b) MFCC based frog identification system in noisy environment. In: 2013 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, pp 123–127Google Scholar
  30. Jang Y, Hahm EH, Lee HJ, Park S, Won YJ, Choe JC (2011) Geographic variation in advertisement calls in a tree frog species: gene flow and selection hypotheses. PloS ONE 6(8):e23–297CrossRefGoogle Scholar
  31. Kular D, Hollowood K, Ommojaro O, Smart K, Bush M, Ribeiro E (2015) Classifying frog calls using Gaussian mixture models. In: Bebis G et al (eds) Advances in visual computing. Springer, Berlin, pp 347–354CrossRefGoogle Scholar
  32. Lee CH, Chou CH, Han CC, Huang RZ (2006) Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis. Pattern Recogn Lett 27(2):93–101CrossRefGoogle Scholar
  33. Noda JJ, Travieso CM, Sánchez-Rodríguez D (2016) Methodology for automatic bioacoustic classification of anurans based on feature fusion. Expert Syst Appl 50:100–106CrossRefGoogle Scholar
  34. Tan W, Jaafar H, Ramli D, Rosdi B, Shahrudin S (2014) Intelligent frog species identification on android operating system. Int J Circuits Syst Signal Process 8:137–148Google Scholar
  35. Tjahja TV, Fern XZ, Raich R, Pham AT (2015) Supervised hierarchical segmentation for bird song recording. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 763–767Google Scholar
  36. Vaca-Castano G, Rodriguez D (2010) Using syllabic Mel cepstrum features and k-nearest neighbors to identify anurans and birds species. In: 2010 IEEE workshop on signal processing systems (SIPS). IEEE, pp 466–471Google Scholar
  37. Wei B, Yang M, Rana RK, Chou CT, Hu W (2012) Distributed sparse approximation for frog sound classification. In: Proceedings of the 11th international conference on information processing in sensor networks. ACM, pp 105–106Google Scholar
  38. Wimmer J, Towsey M, Planitz B, Williamson I, Roe P (2013) Analysing environmental acoustic data through collaboration and automation. Future Gener Comput Syst 29(2):560–568CrossRefGoogle Scholar
  39. Xie J, Towsey M, Eichinski P, Zhang J, Roe P (2015a) Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification. In: 2015 IEEE 11th international conference on e-Science (e-Science). IEEE, pp 237–242Google Scholar
  40. Xie J, Towsey M, Truskinger A, Eichinski P, Zhang J, Roe P (2015b) Acoustic classification of Australian anurans using syllable features. In: 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP). IEEE, pp 1–6Google Scholar
  41. Xie J, Towsey M, Zhang J, Dong X, Roe P (2015c) Application of image processing techniques for frog call classification. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 4190–4194Google Scholar
  42. Xie J, Towsey M, Zhang J, Roe P (2015d) Image processing and classification procedure for the analysis of Australian frog vocalisations. In: Proceedings of the 2nd international workshop on environmental multimedia retrieval. ACM, Shanghai, China, EMR ’15, pp 15–20Google Scholar
  43. Xie J, Towsey M, Zhang J, Roe P (2016) Adaptive frequency scaled wavelet packet decomposition for frog call classification. Ecol Inform 32:134–144CrossRefGoogle Scholar
  44. Yen GG, Fu Q (2002) Automatic frog call monitoring system: a machine learning approach. In: AeroSense 2002. International Society for Optics and Photonics, pp 188–199Google Scholar
  45. Yuan CLT, Ramli DA (2013) Frog sound identification system for frog species recognition. In: Vinh PC et al (eds) Context-aware systems and applications. Springer, Berlin, pp 41–50CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jie Xie
    • 1
  • Michael Towsey
    • 1
  • Jinglan Zhang
    • 1
  • Paul Roe
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia

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