Abstract
In bioacoustics technology, advances such as automated sound recognition based on animal vocalization help in biological research and environmental monitoring. However, in a noisy acoustic environment, where there will be interferences such as overlapping sounds made by multiple species, may greatly hamper the automated sound recognizer performance to identify the specific species. Hence, it is desirable to extract the sound made by the target species from the interferences as a pre-process prior to the recognition to get more accurate results. This paper exploits two Blind Source Separation (BSS) algorithms namely Info-max and FastICA to obtain the target frog sounds from the mixtures. The comparison of algorithm performances is expressed according to Signal-to-Interfere (SIR). The empirical simulation results show that FastICA outperforms Infomax in terms of separation quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Narins, P.M., Feng, A.S.: Hearing and Sound Communication in Amphibians, vol. 28. Springer, New York (2006)
Obrist, M.K., Pavan, G., Sueur, J., Riede, K., Llusia, D., Márquez, R.: Bioacoustics approaches in biodiversity inventories. Man. F. Rec. Tech. Protoc. All Taxa Biodivers. Invent. 8,68–99 (2010)
Huang, C.-J., Yang, Y.-J., Yang, D.-X., Chen, Y.-J.: Frog classification using machine learning techniques. Expert Syst. Appl. 36(2), 3737–3743 (2009)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, Final. Vers. 21(1) (2001)
Nikam, S., Deosarkar, S.: Fast ICA based technique for non-invasive fetal ECG extraction. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 60–65 (2016)
Hyvarinen, A.: Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. Neural Netw. Learn. Syst. 10(3), 626–634 (1999)
Learned-Miller, E.G., Fisher III, J.W.: ICA using spacings estimates of entropy. J. Mach. Learn. Res. 4, 1271–1295 (2003)
Bell, A.J., Sejnowski, T.J.: An information-maximisation approach t o blind separation and blind deconvolution, 1034, pp. 1004–1034 (1995)
Rutledge, D.N., Bouveresse, J.-R.D.: Independent components analysis with the JADE algorithm. In: TrAC—Trends in Analytical Chemistry, vol. 50, pp. 22–32. Elsevier (2013)
Zhang, M., Zhu, M., Ma, W.: Implementation of FastICA on DSP for blind source separation. Procedia Eng. 29, 4228–4233 (2012)
Acknowledgements
This work was supported by Universiti Sains Malaysia under Research University (RU) Grant (No:1001.PELECT.8014057).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hassan, N.B., Ramli, D.A. (2019). Biocoustic Sound Separation Based on FastICA and Infomax Algorithms. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_74
Download citation
DOI: https://doi.org/10.1007/978-981-13-6447-1_74
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6446-4
Online ISBN: 978-981-13-6447-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)