Long-Range Bird Species Identification Using Directional Microphones and CNNs
This study explores the integration of directional microphones with convolutional neural networks (CNNs) for long-range bird species identification. By employing directional microphones, we aimed to capture high-resolution audio from specific directions, potentially improving the clarity of bird cal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/6/4/115 |
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| author | Tiago Garcia Luís Pina Magnus Robb Jorge Maria Roel May Ricardo Oliveira |
| author_facet | Tiago Garcia Luís Pina Magnus Robb Jorge Maria Roel May Ricardo Oliveira |
| author_sort | Tiago Garcia |
| collection | DOAJ |
| description | This study explores the integration of directional microphones with convolutional neural networks (CNNs) for long-range bird species identification. By employing directional microphones, we aimed to capture high-resolution audio from specific directions, potentially improving the clarity of bird calls over extended distances. Our approach involved processing these recordings with CNNs trained on a diverse dataset of bird calls. The results demonstrated that the system is capable of systematically identifying bird species up to 150 m, reaching 280 m for species vocalizing at frequencies greater than 1000 Hz and clearly distinct from background noise. The furthest successful detection was obtained at 510 m. While the method showed promise in enhancing the identification process compared to traditional techniques, there were notable limitations in the clarity of the audio recordings. These findings suggest that while the integration of directional microphones and CNNs for long-range bird species identification is promising, further refinement is needed to fully realize the benefits of this approach. Future efforts should focus on improving the audio-capture technology to reduce ambient noise and enhance the system’s overall performance in long-range bird species identification. |
| format | Article |
| id | doaj-art-bbec77fa5e5f4267b366799a50542b61 |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-bbec77fa5e5f4267b366799a50542b612025-08-20T02:00:43ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-10-01642336235410.3390/make6040115Long-Range Bird Species Identification Using Directional Microphones and CNNsTiago Garcia0Luís Pina1Magnus Robb2Jorge Maria3Roel May4Ricardo Oliveira5STRIX, 4450-286 Matosinhos, PortugalSTRIX, 4450-286 Matosinhos, PortugalSTRIX, 4450-286 Matosinhos, PortugalSTRIX, 4450-286 Matosinhos, PortugalNorwegian Institute for Nature Research (NINA), 7485 Trondheim, NorwaySTRIX, 4450-286 Matosinhos, PortugalThis study explores the integration of directional microphones with convolutional neural networks (CNNs) for long-range bird species identification. By employing directional microphones, we aimed to capture high-resolution audio from specific directions, potentially improving the clarity of bird calls over extended distances. Our approach involved processing these recordings with CNNs trained on a diverse dataset of bird calls. The results demonstrated that the system is capable of systematically identifying bird species up to 150 m, reaching 280 m for species vocalizing at frequencies greater than 1000 Hz and clearly distinct from background noise. The furthest successful detection was obtained at 510 m. While the method showed promise in enhancing the identification process compared to traditional techniques, there were notable limitations in the clarity of the audio recordings. These findings suggest that while the integration of directional microphones and CNNs for long-range bird species identification is promising, further refinement is needed to fully realize the benefits of this approach. Future efforts should focus on improving the audio-capture technology to reduce ambient noise and enhance the system’s overall performance in long-range bird species identification.https://www.mdpi.com/2504-4990/6/4/115long-range bird identificationdirectional microphonesconvolutional neural networks (CNNs)acoustic monitoring |
| spellingShingle | Tiago Garcia Luís Pina Magnus Robb Jorge Maria Roel May Ricardo Oliveira Long-Range Bird Species Identification Using Directional Microphones and CNNs Machine Learning and Knowledge Extraction long-range bird identification directional microphones convolutional neural networks (CNNs) acoustic monitoring |
| title | Long-Range Bird Species Identification Using Directional Microphones and CNNs |
| title_full | Long-Range Bird Species Identification Using Directional Microphones and CNNs |
| title_fullStr | Long-Range Bird Species Identification Using Directional Microphones and CNNs |
| title_full_unstemmed | Long-Range Bird Species Identification Using Directional Microphones and CNNs |
| title_short | Long-Range Bird Species Identification Using Directional Microphones and CNNs |
| title_sort | long range bird species identification using directional microphones and cnns |
| topic | long-range bird identification directional microphones convolutional neural networks (CNNs) acoustic monitoring |
| url | https://www.mdpi.com/2504-4990/6/4/115 |
| work_keys_str_mv | AT tiagogarcia longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns AT luispina longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns AT magnusrobb longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns AT jorgemaria longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns AT roelmay longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns AT ricardooliveira longrangebirdspeciesidentificationusingdirectionalmicrophonesandcnns |