Bird call identification using Dynamic kernel based support vector machines and deep neural networks

Deep Chakraborty; Paawan Mukker; Padmanabhan Rajan; A. D. Dileep

doi:10.1109/ICMLA.2016.60

Bird call identification using Dynamic kernel based support vector machines and deep neural networks

Deep Chakraborty
, Paawan Mukker
, Padmanabhan Rajan
, A. D. Dileep

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

32 Citations (Scopus)

Abstract

In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.

Original language	English
Title of host publication	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	280-285
Number of pages	6
ISBN (Electronic)	9781509061662
DOIs	https://doi.org/10.1109/ICMLA.2016.60
Publication status	Published - 31-01-2017
Externally published	Yes
Event	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: 18-12-2016 → 20-12-2016

Publication series

Name	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Conference

Conference	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/Territory	United States
City	Anaheim
Period	18-12-16 → 20-12-16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICMLA.2016.60

Cite this

Chakraborty, D., Mukker, P., Rajan, P., & Dileep, A. D. (2017). Bird call identification using Dynamic kernel based support vector machines and deep neural networks. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 280-285). Article 7838157 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.60

Chakraborty, Deep ; Mukker, Paawan ; Rajan, Padmanabhan et al. / Bird call identification using Dynamic kernel based support vector machines and deep neural networks. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 280-285 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

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title = "Bird call identification using Dynamic kernel based support vector machines and deep neural networks",

abstract = "In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.",

author = "Deep Chakraborty and Paawan Mukker and Padmanabhan Rajan and Dileep, \{A. D.\}",

year = "2017",

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series = "Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016",

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booktitle = "Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016",

address = "United States",

note = "15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 ; Conference date: 18-12-2016 Through 20-12-2016",

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Chakraborty, D, Mukker, P, Rajan, P & Dileep, AD 2017, Bird call identification using Dynamic kernel based support vector machines and deep neural networks. in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016., 7838157, Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 280-285, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18-12-16. https://doi.org/10.1109/ICMLA.2016.60

Bird call identification using Dynamic kernel based support vector machines and deep neural networks. / Chakraborty, Deep; Mukker, Paawan; Rajan, Padmanabhan et al.
Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 280-285 7838157 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Chakraborty, Deep

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AB - In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.

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Chakraborty D, Mukker P, Rajan P, Dileep AD. Bird call identification using Dynamic kernel based support vector machines and deep neural networks. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 280-285. 7838157. (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). doi: 10.1109/ICMLA.2016.60

Bird call identification using Dynamic kernel based support vector machines and deep neural networks

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