Skip to main content

Advertisement

Springer Nature Link
Log in

A novel deep transfer learning models for recognition of birds sounds in different environment

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Automatic detection of calling bird species is advantageous for monitoring the environment on a broad scale, both temporally and spatially. Numerous investigations have been influenced by feature representations employed in the field of automatic voice recognition. In this study, we investigated deep neural networks on a dataset of 12,061 files for voice recognition for 22 bird species. The methodology adopted in the current study deviates from the existing approaches by integrating transfer learning. Also, multiple feature extraction techniques have been used to extract features from audio to analyze bird sounds, including the Fourier Transform, Mel-Spectrogram, and Mel Frequency Cepstral Coefficients. The study’s main objective is to develop intelligent systems that can predict the different species of bird from the collected set of audio data recordings. The current work verifies that deep transfer learning models like ResNet50, DenseNet201, InceptionV3, Xception and Efficient Net can effectively extract and recognize the audio signals from different bird species with significant prediction accuracy. The absolute best classification accuracy is 97.43%, which DenseNet201 and ResNet50 classification model attained on validation set. Also, DenseNet201 incurred least validation loss (0.1080). The Xception model performed best with the training data and achieved 100% training accuracy and incurred least loss (0.0011). Thus, our study brings us a solution to quantify/test deep learning models appropriately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and material

Not applicable.

References

  • 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. Eco Inform 4(4):206–214. https://doi.org/10.1016/j.ecoinf.2009.06.005

    Article  Google Scholar 

  • Bang AV, Rege PP (2017) Recognition of bird species from their sounds using data reduction techniques. In: ACM international conference proceeding series, pp 111–116.

  • Bao L, Cui Y (2005) Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics 21(10):2185–2190. https://doi.org/10.1093/bioinformatics/bti365

    Article  Google Scholar 

  • Benetos E, Stowell D, Plumbley MD (2018) Approaches to complex sound scene analysis. In: Virtanen T, Plumbley MD, Ellis D (eds) Computational analysis of sound scenes and events. Springer, Cham, pp 215–242. https://doi.org/10.1007/978-3-319-63450-0

    Chapter  Google Scholar 

  • Briggs F, Raich R, Fern XZ (2009) Audio classification of bird species: a statistical manifold approach. Proc Int Conf Data Min ICDM. https://doi.org/10.1109/ICDM.2009.65

    Article  Google Scholar 

  • Cai J, Ee D, Pham B, Roe P, Zhang J (2007) Sensor network for the monitoring of ecosystem: bird species recognition. In: Proceedings of the 2007 international conference on intelligent sensors, sensor networks and information processing, ISSNIP, pp 293–298. https://doi.org/10.1109/ISSNIP.2007.4496859

  • Cakir E, Adavanne S, Parascandolo G, Drossos K, Virtanen T (2017) Convolutional recurrent neural networks for bird audio detection. In: Signal processing conference (EUSIPCO), 2017 25th European. IEEE, pp 1744–1748. https://doi.org/10.23919/eusipco.2017.8081508

  • Incze Á, Jancsó HB, Szilagyi Z, Farkas A, Sulyok C (2018) Bird sound recognition using a convolutional neural network. In: SISY 2018—IEEE 16th international symposium on intelligent systems and informatics, proceedings, September 2018, pp 295–300. https://doi.org/10.1109/SISY.2018.8524677

  • Jain N, Gupta V, Shubham S, Madan A, Chaudhary A, Santosh KC (2021) Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06003-9

    Article  Google Scholar 

  • Jancovic P, Kkuer M (2011) Automatic detection and recognition of tonal bird sounds in noisy environments. Eurasip J Adv Signal Process. https://doi.org/10.1155/2011/982936

    Article  Google Scholar 

  • Kahl S, Wilhelm-Stein T, Hussein H, Klinck H, Kowerko D, Ritter M, Eibl M (2017) Large-scale bird sound classification using convolutional neural networks. CEUR workshop proceedings, 1866

  • Koops HV, Van Balen J, Wiering F (2014) A deep neural network approach to the LifeCLEF 2014 bird task. CEUR Workshop Proceedings, vol 1180, pp 634–642

  • Koops HV, Van Balen J, Wiering F (2015) Automatic segmentation and deep learning of bird sounds. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9283, pp 261–267. https://doi.org/10.1007/978-3-319-24027-5_26

  • Kumar Y, Singh N (2017) An automatic speech recognition system for spontaneous Punjabi speech corpus. Int J Speech Technol 20:297–303. https://doi.org/10.1007/s10772-017-9408-2

    Article  Google Scholar 

  • Kumar Y, Singh N, Kumar M, Singh A (2020) AutoSSR: an efficient approach for automatic spontaneous speech recognition model for the Punjabi Language. Soft Comput 25(2):1617–1630

    Article  Google Scholar 

  • Kumar Y, Kaur K, Kaur S (2021) Study of automatic text summarization approaches in different languages. Artif Intell Rev. https://doi.org/10.1007/s10462-021-09964-4

    Article  Google Scholar 

  • Kumar Y, Singh N (2019) A comprehensive view of automatic speech recognition system—a systematic literature review. In: International conference on automation, computational and technology management (ICACTM), pp 168–173. https://doi.org/10.1109/ICACTM.2019.8776714

  • Lee C-H, Lee Y-K, Huang R-Z (2006) Automatic recognition of bird songs using Cepstral coefficients. J Inf Technol Appl 1(1):17–23

    Google Scholar 

  • Matsubayashi S, Suzuki R, Saito F, Murate T, Masuda T, Yamamoto K, Okuno HG (2017) Acoustic monitoring of the great reed warbler using multiple microphone arrays and robot audition. J Robot Mechatron 29:224–235. https://doi.org/10.20965/jrm.2017.p0224

    Article  Google Scholar 

  • Mehyadin AE, Abdulazeez AM, Hasan DA, Saeed JN (2021) Birds sound classification based on machine learning algorithms. Asian J Res Comput Sci, pp 1–11

  • Mhatre TM, Bhattacharjee S (2018) Birds voice classification using ResNet. Int J Eng Develop Res 6(4):2321–9939

    Google Scholar 

  • Mohanty R, Kumar Mallik B, Singh Solanki S (2020) Recognition of bird species based on spike model using bird dataset. Data Brief 29:105301. https://doi.org/10.1016/j.dib.2020.105301

    Article  Google Scholar 

  • Morfi V, Stowell D (2017) Deductive refinement of species labelling in weakly labelled birdsong recordings. In: Proceedings of ICASSP 2017, pp 656–660. IEEE. https://doi.org/10.1109/icassp.2017.7952237

  • Pamuła H, Klaczynski M, Remisiewicz M, Wszolek W, Stowell D (2017) Adaptation of deep learning methods to nocturnal bird audio monitoring. In: LXIV open seminar on acoustics (OSA) 2017, Piekary Slaskie, Poland

  • Pellegrini T (2017) Densely connected CNNs for bird audio detection. In: Proceedings of EUSIPCO 2017, pp 1734–1738. https://doi.org/10.23919/eusipco.2017.8081506

  • Piczak KJ (2016) Recognizing bird species in audio recordings using deep convolutional neural networks. In: CLEF working notes. Springer, Cham, Switzerland, pp 534–543

  • Qian K, Zhang Z, Baird A, Schuller B (2017) Active learning for bird sound classification via a kernel-based extreme learning machine. J Acoust Soc Am 142(4):1796–1804. https://doi.org/10.1121/1.5004570

    Article  Google Scholar 

  • Qian K, Zhang Z, Ringeval F, Schuller B (2015) Bird sounds classification by large scale acoustic features and extreme learning machine. in Proceedings of GlobalSIP, IEEE, Orlando, FL, pp 1317–1321

  • Scott Brandes T (2008) Automated sound recording and analysis techniques for bird surveys and conservation. Bird Conserv Int 18(S1):S163–S173. https://doi.org/10.1017/S0959270908000415

    Article  Google Scholar 

  • Shriharsha, Tushara, Hemavathi (2020) Bird species classification using Deep learning approach. Int Res J Eng Technol, pp 6030–6033

  • Sprengel E, Jaggi M, Kilcher Y, Hofmann T (2016) Audio based bird species identification using deep learning techniques. CEUR Workshop Proc 1609:547–559

    Google Scholar 

  • Stastny J, Munk M, Juranek L (2018) Automatic bird species recognition based on birds vocalization. Eurasip J Audio Speech Music Process. https://doi.org/10.1186/s13636-018-0143-7

    Article  Google Scholar 

  • Stowell D, Plumbley MD (2014) Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ. https://doi.org/10.7717/peerj.488

    Article  Google Scholar 

  • Stowell D, Wood MD, Pamuła H, Stylianou Y, Glotin H (2019) Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge. Methods Ecol Evol 10(3):368–380. https://doi.org/10.1111/2041-210X.13103

    Article  Google Scholar 

  • Supriya PR, Bhat S, Shivani SS (2018) Classification of birds based on their sound patterns using GMM and SVM classifiers. Int Res J Eng Technol 05(2004):4708–4711

    Google Scholar 

  • Tan LN, Alwan A, Kossan G, Cody ML, Taylor CE (2015) Dynamictime warping and sparse representation classification for birdsong phrase classification using limited training data”. J Acoust Soc Am 137(3):1069–1080

    Article  Google Scholar 

  • Thakur A, Jyothi R, Padmanabhan Rajan AD (2017) Rapid bird activity detection using probabilistic sequence kernels. In: Proceedings of EUSIPCO 2017, pp 1754–1758

  • Xie J, Zhu M (2019) Handcrafted features and late fusion with deep learning for bird sound classification. Eco Inform 52(May):74–81. https://doi.org/10.1016/j.ecoinf.2019.05.007

    Article  Google Scholar 

  • Yu H, Sun C, Yang W, Yang X, Zuo X (2015) Al-elm: one uncertaintybased active learning algorithm using extreme learning machine. Neurocomputing 166:140–150

    Article  Google Scholar 

  • Zhang Z, Schuller B (2012) Active learning by sparse instance tracking and classifier confidence in acoustic emotion recognition. In: Proceedings of INTERSPEECH, ISCA, Portland, OR (2012), pp 362–365

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, India

    Yogesh Kumar

  2. Department of Science and Engineering, Model Institute of Engineering and Technology, Jammu, India

    Surbhi Gupta

  3. Department of Computer Science and Engineering, Punjabi University, Patiala, India

    Williamjeet Singh

Authors
  1. Yogesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surbhi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Williamjeet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yogesh Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 194 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, Y., Gupta, S. & Singh, W. A novel deep transfer learning models for recognition of birds sounds in different environment. Soft Comput 26, 1003–1023 (2022). https://doi.org/10.1007/s00500-021-06640-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06640-1

Keywords