Refine your search
Collections
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Akhter, Shamim
- Identify the Beehive Sound Using Deep Learning
Abstract Views :83 |
PDF Views:69
Authors
Affiliations
1 Department of Computer Science, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MY
2 AISIP Lab, Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, BD
3 Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka, BD
1 Department of Computer Science, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MY
2 AISIP Lab, Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, BD
3 Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka, BD
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 14, No 4 (2022), Pagination: 13-29Abstract
Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering plants involves pollination, fertilization, flowering, seed-formation, dispersion, and germination. Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change, natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the non-beehive noises. In addition, we perform a comparative study among some popular non-deep learning techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).Keywords
Beehive Sound Recognition, Audio Data Feature Extraction, Sequential Neural Network, Recurrent Neural Network, Convolutional Neural Network.References
- D. Stowell, “Computational Bioacoustic Scene Analysis,” in Computational Analysis of Sound Scenes and Events, Cham: Springer International Publishing, 2018, pp. 303–333.
- M. Bencsik, J. Bencsik, M. Baxter, A. Lucian, J. Romieu, and M. Millet, “Identification of the honey bee swarming process by analysing the time course of hive vibrations,” Comput. Electron. Agric., vol. 76, no. 1, pp. 44–50, 2011.
- A. Zacepins, A. Kviesis, E. Stalidzans, M. Liepniece, and J. Meitalovs, “Remote detection of the swarming of honey bee colonies by single-point temperature monitoring,” Biosyst. Eng., vol. 148, pp. 76–80, 2016.
- A. Robles-Guerrero, T. Saucedo-Anaya, E. González-Ramérez, and C. E. Galván-Tejada, “Frequency analysis of honey bee buzz for automatic recognition of health status: A preliminary study,” Research in Computing Science, vol. 142, no. 1, pp. 89–98, 2017.
- S. Ferrari, M. Silva, M. Guarino, and D. Berckmans, “Monitoring of swarming sounds in bee hives for early detection of the swarming period,” Comput. Electron. Agric., vol. 64, no. 1, pp. 72–77, 2008.
- P. Amlathe, “Standard machine learning techniques in Audio Beehive Monitoring: Classification of audio samples with logistic regression, K-nearest neighbor, random forest and support vector machine,” DigitalCommons@USU, https://digitalcommons.usu.edu/etd/7050/.
- J. Li, W. Dai, F. Metze, S. Qu, and S. Das, “A comparison of Deep Learning methods for environmental sound detection,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
- R. Serizel, V. Bisot, S. Essid, and G. Richard, “Acoustic features for environmental sound analysis,” in Computational Analysis of Sound Scenes and Events, Cham: Springer International Publishing, 2018, pp. 71–101.
- A. Zgank, “IoT-based bee swarm activity acoustic classification using deep neural networks,” Sensors (Basel), vol. 21, no. 3, p. 676, 2021.
- J. Kim, J. Oh, and T.-Y. Heo, “Acoustic scene classification and visualization of beehive sounds using machine learning algorithms and Grad-CAM,” Math. Probl. Eng., vol. 2021, pp. 1–13, 2021.
- I. Nolasco and E. Benetos, “To bee or not to bee: Investigating machine learning approaches for beehive sound recognition,” arXiv [cs.SD], 2018.
- A. Terenzi, S. Cecchi, and S. Spinsante, “On the importance of the sound emitted by honey bee hives,” Vet. Sci., vol. 7, no. 4, p. 168, 2020.
- T. Cejrowski, J. Szymański, and D. Logofătu, “Buzz-based recognition of the honeybee colony circadian rhythm,” Comput. Electron. Agric., vol. 175, no. 105586, p. 105586, 2020.
- T. Sledevi c ˇ, ”The application of convolutional neural network for pollen bearing bee classification,”2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), pp. 1-4, IEEE, Nov. 2018.
- A. Terenzi, N. Ortolani, I. Nolasco, E. Benetos, and S. Cecchi, “Comparison of feature extraction methods for sound-based classification of honey bee activity,” IEEE ACM Trans. Audio Speech Lang. Process., vol. 30, pp. 112–122, 2022.
- V. Kulyukin, S. Mukherjee, and P. Amlathe, “Toward audio beehive monitoring: Deep learning vs. Standard Machine Learning in classifying beehive audio samples,” Applied Sciences, vol. 8, no. 9, p. 1573, 2018.
- A. Qandour, I. Ahmad, D. Habibi, and M. Leppard, “Remote Beehive monitoring using acoustic signals,” Research Online, https://ro.ecu.edu.au/ecuworkspost2013/506/.
- K. Dineva, “Applying machine learning against beehives dataset,” in 18th International Multidisciplinary Scientific GeoConference SGEM2018, Nano, Bio and Green � Technologies for a Sustainable Future, 2018.
- J. J. Noda, C. M. Travieso-González, D. Sánchez-Rodríguez, and J. B. Alonso-Hernández, “Acoustic classification of singing insects based on MFCC/LFCC fusion,” Appl. Sci. (Basel), vol. 9, no. 19, p. 4097, 2019.
- A. P. Ribeiro, N. F. F. da Silva, F. N. Mesquita, P. de C. S. Araújo, T. C. Rosa, and J. N. Mesquita-Neto, “Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds,” PLoSComput. Biol., vol. 17, no. 9, p. e1009426, 2021.
- A. Zgank, “Acoustic monitoring and classification of bee swarm activity using MFCC feature extraction and HMM acoustic modeling,” in 2018 ELEKTRO, 2018.
- S. Ahad Zolfagharifar, F. Karamizadeh, and H. Parvin, “Providing a combination classification (honeybee Clooney and decision tree) based on developmental learning,” Mod. Appl. Sci., vol. 9, no. 13, p. 188, 2015.