Difference between revisions of "Bird Identification"
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|description=Helpful resources for your journey with artificial intelligence; chat, chatbot, videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; chat, chatbot, videos, articles, techniques, courses, profiles, and tools | ||
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| − | [https://www.youtube.com/results?search_query= | + | [https://www.youtube.com/results?search_query=Merlin+Bird+ID+eBird+Cornell+Lab+Ornithology YouTube] |
| − | [https://www.quora.com/search?q= | + | [https://www.quora.com/search?q=Merlin%20Bird%20ID%20eBird%20Cornell%20Lab%20Ornithology ... Quora] |
| − | [https://www.google.com/search?q= | + | [https://www.google.com/search?q=Merlin+Bird+ID+eBird+Cornell+Lab+Ornithology ...Google search] |
| − | [https://news.google.com/search?q= | + | [https://news.google.com/search?q=Merlin+Bird+ID+eBird+Cornell+Lab+Ornithology ...Google News] |
| − | [https://www.bing.com/news/search?q= | + | [https://www.bing.com/news/search?q=Merlin+Bird+ID+eBird+Cornell+Lab+Ornithology&qft=interval%3d%228%22 ...Bing News] |
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| − | |||
* [[Capabilities]] | * [[Capabilities]] | ||
** [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]] ... [[Music]] | ** [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]] ... [[Music]] | ||
** [[Video/Image]] ... [[Vision]] ... [[Colorize]] ... [[Image/Video Transfer Learning]] | ** [[Video/Image]] ... [[Vision]] ... [[Colorize]] ... [[Image/Video Transfer Learning]] | ||
| + | = Merlin = | ||
| + | * [https://merlin.allaboutbirds.org Merlin] | ||
| + | ** [https://play.google.com/store/search?q=merlin+bird+app&c=apps&hl=en Merlin | Google Play] | ||
| + | ** [https://apps.apple.com/app/apple-store/id773457673 Merlin | Apple Store] | ||
<b>Photo ID</b>: Merlin Photo ID uses computer vision technology, developed as part of Dr. Grant Van Horn’s doctoral work at Caltech, to identify birds in photos. Photo ID was developed in collaboration with Dr. Pietro Perona’s computational vision lab at Caltech, and Dr. Serge Belongie’s computer vision group at Cornell Tech, collaborators on the Visipedia project. First publicly released Nov 30th, 2017. | <b>Photo ID</b>: Merlin Photo ID uses computer vision technology, developed as part of Dr. Grant Van Horn’s doctoral work at Caltech, to identify birds in photos. Photo ID was developed in collaboration with Dr. Pietro Perona’s computational vision lab at Caltech, and Dr. Serge Belongie’s computer vision group at Cornell Tech, collaborators on the Visipedia project. First publicly released Nov 30th, 2017. | ||
<b>Sound ID</b> Sound ID uses recordings archived in the Macaulay Library to learn how to recognize the vocalizations of different bird species. Sound ID is trained on audio recordings that are first converted to visual representations (spectrograms), then analyzed using computer vision tools similar to those that power Photo ID. Dataset preparation began in 2020 with model development starting in early 2021. Sound ID was developed in-house at the Cornell Lab of Ornithology, led by Dr. Grant Van Horn with assistance from Dr. Benjamin Hoffman. S We thank the many annotators that helped curate hundreds of audio recordings for each species. First publicly release June 23rd, 2021. | <b>Sound ID</b> Sound ID uses recordings archived in the Macaulay Library to learn how to recognize the vocalizations of different bird species. Sound ID is trained on audio recordings that are first converted to visual representations (spectrograms), then analyzed using computer vision tools similar to those that power Photo ID. Dataset preparation began in 2020 with model development starting in early 2021. Sound ID was developed in-house at the Cornell Lab of Ornithology, led by Dr. Grant Van Horn with assistance from Dr. Benjamin Hoffman. S We thank the many annotators that helped curate hundreds of audio recordings for each species. First publicly release June 23rd, 2021. | ||
| + | |||
| + | = eBird = | ||
| + | [https://ebird.org/home eBird | Cornell Lab of Ornithology] | ||
| + | |||
| + | eBird began with a simple idea—that every birdwatcher has unique knowledge and experience. Our goal is to gather this information in the form of checklists of birds, archive it, and freely share it to power new data-driven approaches to science, conservation and education. At the same time, we develop tools that make birding more rewarding. From being able to manage lists, photos and audio recordings, to seeing real-time maps of species distribution, to alerts that let you know when species have been seen, we strive to provide the most current and useful information to the birding community. | ||
| + | |||
| + | eBird is among the world’s largest biodiversity-related science projects, with more than 100 million bird sightings contributed annually by eBirders around the world and an average participation growth rate of approximately 20% year over year. A collaborative enterprise with hundreds of partner organizations, thousands of regional experts, and hundreds of thousands of users, eBird is managed by the Cornell Lab of Ornithology. | ||
Revision as of 09:11, 11 June 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Capabilities
- End-to-End Speech ... Synthesize Speech ... Speech Recognition ... Music
- Video/Image ... Vision ... Colorize ... Image/Video Transfer Learning
Merlin
Photo ID: Merlin Photo ID uses computer vision technology, developed as part of Dr. Grant Van Horn’s doctoral work at Caltech, to identify birds in photos. Photo ID was developed in collaboration with Dr. Pietro Perona’s computational vision lab at Caltech, and Dr. Serge Belongie’s computer vision group at Cornell Tech, collaborators on the Visipedia project. First publicly released Nov 30th, 2017.
Sound ID Sound ID uses recordings archived in the Macaulay Library to learn how to recognize the vocalizations of different bird species. Sound ID is trained on audio recordings that are first converted to visual representations (spectrograms), then analyzed using computer vision tools similar to those that power Photo ID. Dataset preparation began in 2020 with model development starting in early 2021. Sound ID was developed in-house at the Cornell Lab of Ornithology, led by Dr. Grant Van Horn with assistance from Dr. Benjamin Hoffman. S We thank the many annotators that helped curate hundreds of audio recordings for each species. First publicly release June 23rd, 2021.
eBird
eBird | Cornell Lab of Ornithology
eBird began with a simple idea—that every birdwatcher has unique knowledge and experience. Our goal is to gather this information in the form of checklists of birds, archive it, and freely share it to power new data-driven approaches to science, conservation and education. At the same time, we develop tools that make birding more rewarding. From being able to manage lists, photos and audio recordings, to seeing real-time maps of species distribution, to alerts that let you know when species have been seen, we strive to provide the most current and useful information to the birding community.
eBird is among the world’s largest biodiversity-related science projects, with more than 100 million bird sightings contributed annually by eBirders around the world and an average participation growth rate of approximately 20% year over year. A collaborative enterprise with hundreds of partner organizations, thousands of regional experts, and hundreds of thousands of users, eBird is managed by the Cornell Lab of Ornithology.
