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DC Field | Value | Language |
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dc.contributor.author | Raghuram, M.A. | |
dc.contributor.author | Chavan, N.R. | |
dc.contributor.author | Belur, R. | |
dc.contributor.author | Koolagudi, S.G. | |
dc.date.accessioned | 2020-03-31T08:18:36Z | - |
dc.date.available | 2020-03-31T08:18:36Z | - |
dc.date.issued | 2016 | |
dc.identifier.citation | International Journal of Speech Technology, 2016, Vol.19, 4, pp.791-804 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/10100 | - |
dc.description.abstract | In this paper we focus on automatic bird classification based on their sound patterns. This is useful in the field of ornithology for studying bird species and their behavior based on their sound. The proposed methodology may be used to conduct survey of birds. The proposed methods may be used to automatically classify birds using different audio processing and machine learning techniques on the basis of their chirping patterns. An effort has been made in this work to map characteristics of birds such as size, habitat, species and types of call, on to their sounds. This study is also part of a broader project that includes development of software and hardware systems to monitor the bird species that appear in different geographical locations which helps ornithologists to monitor environmental conditions with respect to specific bird species. 2016, Springer Science+Business Media New York. | en_US |
dc.title | Bird classification based on their sound patterns | en_US |
dc.type | Article | en_US |
Appears in Collections: | 1. Journal Articles |
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