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Welcome to the top-level wiki for Orcasound! 
Here you can learn about Orcasound and how to listen for whales. Everyone should read the overview and then proceed to the onboarding section. What is Orcasound? Orcasound is a hydrophone network in the critical habitat of the endangered Southern Resident killer whales. We began near Seattle (WA, USA), but our goal is to monitor their entire range -- from British Columbia to northern California -- for their calls, whistles, and clicks, as well as noise pollution that could harm them. Orcasound is an open source community building free hardware and software solutions for bioacoustics and marine conservation. We use low-cost underwater microphone systems to listen for whales continuously through an app for community scientists which is assisted by artificial intelligence. 
Orcasound is powered by volunteerism. Some folks are divers, captains, or general volunteers who help their favorite NGO deploy and maintain a new hydrophone. Some are software designers and developers who contribute via hackathons, summer internships, or asynchronous teams. Others are retirees or working professionals who bring expertise in project management, data science, artificial intelligence, or bioacoustics to the table and build innovative new solutions for science and saving soniferous species. Orcasound is a do-ocracy. If you can build it with open source tools and/or cheap hardware -- and your innovative solution works for science and sustainability -- how you do it (or hack it together) doesn't matter so much to us. Learn more about our community (code of conduct, organization, governance, resources, funding, acknowledgments, recruiting, Hall of Fame) Now that you know a bit about Orcasound and what we expect of you as you join our community, it's time to introduce yourself so we can begin to help you make a meaningful contribution to our efforts. We use the open source platform Zulip for community communication. You are welcome to browse Orcasound's open Zulip channels. If you're intrigued with what you see, you're invited to join Orcasound's Zulip chat and introduce yourself in the How do you want to contribute? In your introduction, let us know (1) where you're based, (2) how you found us, and (3) which role (or roles) you'd like to play:
Of course, if none of these seem like a good fit, you're also welcome to suggest a new role that you'd like to pioneer! Practical technical guidance for monitoring & maintaining currently-deployed Orcasound software, deployments, and analytics. If you're an Orcasound administrator, or would like to be one, the following sections will lead you to critical how-to information across the Orcasound ecosystem. The core Orcasound software is a suite of tools that make it easy to listen to live audio streams within any web browser. Because the audio data can be mapped to a date-time, the player in the web app knows the real time of the audio a listener is hearing, so community scientists can use the web app to help to annotate the live audio data. The Orcanode software generates the live audio stream from a low-cost single-board computer. We are agnostic to the hardware selected, but have mostly tested and deployed on Raspberry Pis (model 3b or 4). The Dockerized code is written in shell script or Python. The Orcanode wiki covers:
The Orcasite software is a browser-based web app that is deployed via Heroku and pulls audio data from Amazon S3 buckets. The Orcasite wiki's administrative section covers:
A long-term goal of the Orcasound web app is to facilitate signal detection in live audio data by an optimal combination of human listeners and Artificial Intelligence agents. To that end we have built a machine learning pipeline that iteratively harvests live audio data that humans find interesting (indicated with a button in v2) and/or that initial models think contain orca sounds. To speed the iterative process, we have also developed annotation tools to more efficiently label our highest-value archived data (see Annotation projects below). With increasing amounts of labeled data in hand, we have developed and are refining a real time inference system that utilizes a binary classifier to predict if a detected signal is a call of the endangered Southern Resident killer whales (SRKWs), or not. Dubbed "OrcaHello" by the Microsoft hacakthon volunteers who built it, the system is deployed via Azure services working from the raw audio data S3 buckets. The OrcaHello software is maintained in the aifororcas-livesystem repo. The aifororcas-livesystem wiki has a OrcaHello Administration page that covers:
Multiple Orcasound projects run on Azure and generate costs for Orcasound. A cost analysis can be obtained by those with permission, as follows:
You can also see the history of per-month costs as follows:
Maybe this is more of a catch-all troubleshooting guide or FAQ aimed at concerned community scientists persona? Or a general FAQ broken down by persona? Concerned community scientist FAQ:
Professional conservation scientist FAQ:
A guide for replicating Orcasound for another region or species So you, too, would like to study and help save a species that makes captivating sounds underwater? Luckily, Orcasound was built from the ground up with your goal in mind. We aspire to provide free access to our hardware and software innovations, and we try hard to document our organization, methods, and materials so you can follow our lead -- or build upon our foundation so we can follow your lead! This guide will give you some ideas for how to organize a new hydrophone network along with tips on optimal site selection strategies. The scientific questions you seek to answer, management goals, or educational/outreach dreams often can help guide what hardware & software options you choose, and limit startup costs. Once you have a plan, we offer details about where to get tried-and-tested hardware and how to "clone" or "fork" our software repositories and then deploy the code. Mostly our code provides ways of streaming audio data from the ocean through the cloud to your network of listeners (humans and/or machines), but we are also catalyzing new ways to create and share data products, and provide bioacoustic and noise analyses. Outline (for linked page):
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