PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

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Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J

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  • 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

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