PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 29 (4) Oct. 2021 / JST-2440-2021

 

A Novel Approach of Audio Based Feature Optimisation for Bird Classification

Murugaiya Ramashini, Pg Emeroylariffion Abas and Liyanage C De Silva

Pertanika Journal of Tropical Agricultural Science, Volume 29, Issue 4, October 2021

DOI: https://doi.org/10.47836/pjst.29.4.08

Keywords: Artificial neural network (ANN), bird sounds, classification, linear discriminant analysis (LDA), nearest centroid (NC)

Published on: 29 October 2021

Bird classification using audio data can be beneficial in assisting ornithologists, bird watchers and environmentalists. However, due to the complex environment in the jungles, it is difficult to identify birds by visual inspection. Hence, identification via acoustical means may be a better option in such an environment. This study aims to classify endemic Bornean birds using their sounds. Thirty-five (35) acoustic features have been extracted from the pre-recorded soundtracks of birds. In this paper, a novel approach for selecting an optimum number of features using Linear Discriminant Analysis (LDA) has been proposed to give better classification accuracy. It is found that using a Nearest Centroid (NC) technique with LDA produces the optimum classification results of bird sounds at 96.7% accuracy with reduced computational power. The low computational complexity is an added advantage for handheld portable devices with minimal computing power, which can be used in birdwatching expeditions. Comparison results have been provided with and without LDA using NC and Artificial Neural Network (ANN) classifiers. It has been demonstrated that both classifiers with LDA outperform those without LDA. Maximum accuracies for both NC and ANN with LDA, with NC and the ANN classifiers requiring 7 and 10 LDAs to achieve the optimum accuracy, respectively, are 96.7%. However, ANN classifier with LDA is more computationally complex. Hence, this is significant as the simpler NC classifier with LDA, which does not require expensive processing power, may be used on the portable and affordable device for bird classification purposes.

  • Alvarsson, J. J., Wiens, S., & Nilsson, M. E. (2010). Stress recovery during exposure to nature sound and environmental noise. International Journal of Environmental Research and Public Health, 7(3), 1036-1046. https://doi.org/10.3390/ijerph7031036

  • Anderson, S. E., Dave, A. S., & Margoliash, D. (1996). Template‐based automatic recognition of birdsong syllables from continuous recordings. The Journal of the Acoustical Society of America, 100(2), 1209-1219. https://doi.org/10.1121/1.415968

  • Badi, A., Ko, K., & Ko, H. (2019). Bird sounds classification by combining PNCC and robust Mel-log filter bank features. Journal of the Acoustical Society of Korea, 38(1), 39-46. https://doi.org/10.7776/ASK.2019.38.1.039

  • Butler, R. W. (2019). Niche tourism (birdwatching) and its impacts on the well-being of a remote island and its residents. International Journal of Tourism Anthropology, 7(1), 5-20. https://doi.org/10.1504/ijta.2019.10019435

  • Chou, C. H., Liu, P. H., & Cai, B. (2008). On the studies of syllable segmentation and improving MFCCs for automatic birdsong recognition. In Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008 (pp. 745-750). IEEE Publishing. https://doi.org/10.1109/APSCC.2008.6

  • Elliott, D. L. (1993). A better activation function for artificial neural networks. ISR Technical Report TR 93-8. Neuro Dyne, Inc.

  • Evangelista, T. L., Priolli, T. M., Silla, C. N., Angelico, B. A., & Kaestner, C. A. (2015). Automatic segmentation of audio signals for bird species identification. In 2014 IEEE International Symposium on Multimedia (pp. 223-228). IEEE Publishing. https://doi.org/10.1109/ISM.2014.46

  • Fagerlund, S. (2007). Bird species recognition using support vector machines. EURASIP Journal on Advances in Signal Processing, 2007, 1-8. https://doi.org/10.1155/2007/38637

  • Fagerlund, S., & Laine, U. K. (2014). New parametric representations of bird sounds for automatic classification. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (ICASSP) (pp. 8247-8251). IEEE Publishing. https://doi.org/10.1109/ICASSP.2014.6855209

  • Gerhard, D. (2003). Audio signal classification: History and current techniques. Technical Report TR-CS 2003-07. University of Regina.

  • Giannakopoulos, T., & Pikrakis, A. (2014). Introduction to audio analysis: A MATLAB® approach. Academic Press.

  • Härmä, A., Somervuo, P., Harma, A., & Somervuo, P. (2004). Classification of the harmonic structure in bird vocalization. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 5, pp. V-701). IEEE Publishing. https://doi.org/10.1109/ICASSP.2004.1327207

  • Kutzner, D. (2019). Environmental change, resilience, and adaptation in nature-based tourism: Conceptualizing the social-ecological resilience of birdwatching tour operations. Journal of Sustainable Tourism, 27(8), 1142-1166. https://doi.org/10.1080/09669582.2019.1601730

  • Lasseck, M. (2015). Improved automatic bird identification through decision tree based feature selection and bagging. LifeCLEF. Museum für Naturkunde Berlin.

  • Lee, C. H., Han, C. C., & Chuang, C. C. (2008). Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients. IEEE Transactions on Audio, Speech and Language Processing, 16(8), 1541-1550. https://doi.org/10.1109/TASL.2008.2005345

  • Ludeña-Choez, J., Quispe-Soncco, R., & Gallardo-Antolín, A. (2017). Bird sound spectrogram decomposition through non-negative matrix factorization for the acoustic classification of bird species. PLoS ONE, 12(6), 1-20. https://doi.org/10.1371/journal.pone.0179403

  • Martinez, A. M., & Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228-233. https://doi.org/10.1109/34.908974

  • McIlraith, A. L., & Card, H. C. (1997). Birdsong recognition using backpropagation and multivariate statistics. IEEE Transactions on Signal Processing, 45(11), 2740-2748. https://doi.org/10.1109/78.650100

  • Milani, M. G. M., Abas, P. E., & De Silva, L. C. (2019). Identification of normal and abnormal heart sounds by prominent peak analysis. In Proceedings of the 2019 International Symposium on Signal Processing Systems (pp. 31-35). Association for Computing Machinery. https://doi.org/10.1145/3364908.3364924

  • Mogi, R., & Kasai, H. (2013). Noise-Robust environmental sound classification method based on combination of ICA and MP features. Journal of Artificial Intelligence Research, 2(1), 107-121. https://doi.org/10.5430/air.v2n1p107

  • Priyadarshani, N., Marsland, S., & Castro, I. (2018). Automated birdsong recognition in complex acoustic environments: A review. Journal of Avian Biology, 49(5), 1-27. https://doi.org/10.1111/jav.01447

  • Ramashini, M., Abas, P. E., Grafe, U., & De Silva, L. C. (2019). Bird sounds classification using linear discriminant analysis. In 2019 4th International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/ICRAIE47735.2019.9037645

  • Ranjard, L., & Ross, H. A. (2008). Unsupervised bird song syllable classification using evolving neural networks. The Journal of the Acoustical Society of America, 123(6), 4358-4368. https://doi.org/10.1121/1.2903861

  • Selouani, S. A. S. A., Kardouchi, M., Hervet, É., Roy, D., Hervet, E., & Roy, D. (2005). Automatic birdsong recognition based on autoregressive time-delay neural networks. In 2005 ICSC Congress on Computational Intelligence Methods and Applications (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/CIMA.2005.1662316

  • Sharma, G., Umapathy, K., & Krishnan, S. (2020). Trends in audio signal feature extraction methods. Applied Acoustics, 158, Article 107020. https://doi.org/10.1016/j.apacoust.2019.107020

  • Sprengel, E., Jaggi, M., Kilcher, Y., & Hofmann, T. (2016). Audio Based Bird Species Identification using Deep Learning Techniques. In Working Notes of CLEF 2016 (pp. 547-559). Cross Language Evaluation Forum.

  • Stowell, D., & Plumbley, M. D. (2014). Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ, 2, Article e488. https://doi.org/10.7717/peerj.488

  • Suthers, R. A. (2004). How birds sing and why it matters. In Nature’s Music: The Science of Birdsong (pp. 272-295). Elsevier Academic Press. https://doi.org/10.1016/B978-012473070-0/50012-8

  • Tan, L. N., Kaewtip, K., Cody, M. L., Taylor, C. E., & Alwan, A. (2012). Evaluation of a Sparse Representation-Based Classifier For Bird Phrase Classification Under Limited Data Conditions. In Thirteenth Annual Conference of the International Speech Communication Association (pp. 2522-2525). International Speech Communication Association (ISCA).

  • Terry, A. M. R., & McGregor, P. K. (2002). Census and monitoring based on individually identifiable vocalizations: The role of neural networks. Animal Conservation, 5(2), 103-111. https://doi.org/10.1017/S1367943002002147

  • Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A. E. (2017). Linear discriminant analysis: A detailed tutorial. AI Communications, 30(2), 169-190. https://doi.org/10.3233/AIC-170729

  • Trifa, V. M., Kirschel, A. N. G., Taylor, C. E., & Vallejo, E. E. (2008). Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. The Journal of the Acoustical Society of America, 123(4), 2424-2431. https://doi.org/10.1121/1.2839017

  • Vilches, E., Escobar, I. A., Vallejo, E. E., & Taylor, C. E. (2006). Data mining applied to acoustic bird species recognition. In 18th International Conference on Pattern Recognition (ICPR’06) (Vol. 3, pp. 400-403). IEEE Publishing. https://doi.org/10.1109/ICPR.2006.426

ISSN 1511-3701

e-ISSN 2231-8542

Article ID

JST-2440-2021

Download Full Article PDF

Share this article

Recent Articles