Cloud-based deployment of OOI hydrophone streaming #62
Description
2022 GSoC project by @karan2704 developed code to convert audio data in mseed format from the public Ocean Observatories Initiative(OOI) hydrophones into a format suitable to stream from the Orcasound website. This project aims to deploy the streaming on the cloud for continuous listening, test the robustness of the system, and integrate with the Orcasound website.
Test deployment can be run on AWS instance, however, as part of the project it would be helpful to also investigate how to minimize the costs by using other services.
Expected outcomes: Increase access to NSF-funded audio data from hydrophones in killer whale habitat on the outer coast, opening the door to wintertime acoustic detections of endangered orcas.
Required skills: Python, Docker, Cloud Computing
Bonus skills: Audio Processing, javascript, GIthub Actions
Mentors: Valentina, Karan
Difficulty level: Medium
Project Size: 175 or 350 h
Resources:
- Orcanode repo
- Orcanode wiki
- Karan's blog post
- Karan's final report
Getting Started:
Run the docker setup locally.
Run the github workflows on your fork.
Points to consider in the proposal:
What cloud tools you can use to continuously pull the data?
How do you optimize the performance? Minimize cost?
How do you set up the cloud infrastructure so that it is scriptable/cloud-agnostic?