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Visualize human vs machine classifications in time & space #5
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Based on the References and open-source building blocks mentioned in the project, I had developed a Visualization Dashboard through React ( https://dhananjaypurohit.github.io/OrcaDashboard/ ). I had used Google Sheets (OrcaData) to hold the same Automated detection data of a week given on the website and had implemented some visualizations on the same data. |
Here is the link to my proposal. |
Hey @scottveirs! This seemed to be an interesting project within my limited capabilities. I’m looking to contribute towards this as it was an intriguing project (i.e. I don’t want to do this for google summer of code or any other purpose). I would love to collaborate with you regarding the same. If I am allowed to contribute please comment here (I’m new to open source). I’d love to share a roadmap with you before I begin and would love to be mentored by your team. Moreover if this goes well, I’d like to be a part of the Orcasound team on a much more permanent basis too. Looking forward to hearing from you! Cheers |
Add list of emerging sources of species presence inferred from acoustic signals. |
An April 2021 note from Akash regarding this GSoC topic: On this - everything is already on Github GitHub - orcasound/aifororcas-livesystem, just not documented to a complete level, although it seems like good progress was made in March (see the open PRs). Building over what exists is possible for 2021 itself. Might be a bit messy but folks are most welcome to bite the bullet sooner.
FYI - (this is a closed source tool within Azure/MFST ecosystem) - i've seen people use Data Visualization | Microsoft Power BI to connect with existing resources and quickly spin up some dashboards. |
Visualize human vs machine classifications in time & space
Take data outputs from ML models and/or human detection from the Orcasound app and visualize them to provide bioacoustic insights.
Many biological sounds made by marine organisms are correlated with important temporal cycles in the ocean, e.g. tides, daylight, moonlight, seasons, decadal trends. As Orcasound continues to generate human detections over the years, and as more ML models classify sounds in the audio streams, we’d like to visualize their patterns. We’re most interested in how the soundscape changes over time, but you could begin to look for spatial patterns, too (across the three locations that Orcasound currently maintains and the 5 that we expect to have deployed by the end of 2020). See the “Data visualization ideas” list in Orcasound’s Trello board of “bioacoustic data analysis approaches".
Required skills: Elixir, Javascript
Bonus skills: time-series analysis
Possible mentors: Scott, Val, Jesse
References and open-source building blocks:
Good visualization ideas from the Santa Barbara Channel hydrophone (real-time ML outputs)
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