Furnariidae species recognition using speech-related features and machine learning
The automatic classification of calling bird species is important to achieve more exhaustive environmental monitoring and to manage natural resources. Bird vocalizations allow to recognise new species, their natural history and macro-systematic relations, while automatic systems can speed up and imp...
| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2016
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/56982 http://45jaiio.sadio.org.ar/sites/default/files/ASAI-15_0.pdf |
| Aporte de: |
| id |
I19-R120-10915-56982 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas bird calls classification computational bioacoustics machine learning speech-related features furnariidae |
| spellingShingle |
Ciencias Informáticas bird calls classification computational bioacoustics machine learning speech-related features furnariidae Vignolo, Leandro Sarquis, Juan A. León, Evelina Albornoz, Enrique Furnariidae species recognition using speech-related features and machine learning |
| topic_facet |
Ciencias Informáticas bird calls classification computational bioacoustics machine learning speech-related features furnariidae |
| description |
The automatic classification of calling bird species is important to achieve more exhaustive environmental monitoring and to manage natural resources. Bird vocalizations allow to recognise new species, their natural history and macro-systematic relations, while automatic systems can speed up and improve all the process. In this work, we use state-of-art features designed for speech and speaker state recognition to classify 25 species of Furnariidae family. Since Furnariidae species inhabit the Litoral Paranaense region of Argentina (South America), this work could promote further research on the topic and the implementation of in-situ monitoring systems. Our analysis includes two widely-known classification techniques: random forest an support vector machines. The results are promising, near 86%, and were validated in a cross-validation scheme. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Vignolo, Leandro Sarquis, Juan A. León, Evelina Albornoz, Enrique |
| author_facet |
Vignolo, Leandro Sarquis, Juan A. León, Evelina Albornoz, Enrique |
| author_sort |
Vignolo, Leandro |
| title |
Furnariidae species recognition using speech-related features and machine learning |
| title_short |
Furnariidae species recognition using speech-related features and machine learning |
| title_full |
Furnariidae species recognition using speech-related features and machine learning |
| title_fullStr |
Furnariidae species recognition using speech-related features and machine learning |
| title_full_unstemmed |
Furnariidae species recognition using speech-related features and machine learning |
| title_sort |
furnariidae species recognition using speech-related features and machine learning |
| publishDate |
2016 |
| url |
http://sedici.unlp.edu.ar/handle/10915/56982 http://45jaiio.sadio.org.ar/sites/default/files/ASAI-15_0.pdf |
| work_keys_str_mv |
AT vignololeandro furnariidaespeciesrecognitionusingspeechrelatedfeaturesandmachinelearning AT sarquisjuana furnariidaespeciesrecognitionusingspeechrelatedfeaturesandmachinelearning AT leonevelina furnariidaespeciesrecognitionusingspeechrelatedfeaturesandmachinelearning AT albornozenrique furnariidaespeciesrecognitionusingspeechrelatedfeaturesandmachinelearning |
| bdutipo_str |
Repositorios |
| _version_ |
1764820476777988103 |