Early history of U.S. Salish Sea hydrophone networks

Extracted from Chronology of hydrophones in the Salish Sea (U.S. and Canada, including Johnstone Strait) '70s: Single hydrophones: -- Rich Osborne (The Whale Museum) at Lime Kiln lighthouse; -- Ken Balcomb at the Center for Whale Research 80s-90s: -- Localization array at Lime Kiln (Rich, David Bain, and Joe Olson); -- Vashon Hydrophone Project of the American Cetacean Society ~2001-2004: Val and Colorado College students establish Orcasound Lab between Lime Kiln and Roche Harbor ~2005-2012: Beam Reach students use and help maintain hydrophones at Lime Kiln (Jason Wood, then Beam Reach faculty and/or Research Director at TWM) and Orcasound Lab, with funding assistance from WDFW, Beam Reach, and Cornell University

Neptune's fist!

Orcasound financial history

From government grants to crowdfunding

Orcasound organization

A cooperative of non-profit education/outreach organizations, tech specialists, and researchers

Orcasound hydrophone network organizational members (as of fall 2018):

        Beam Reach, SPC
        Colorado College, Physics & Environmental Science
        Orca Network
        Port Townsend Marine Science Center
        Orca Behavior Institute
        Cetacean Research Technology
        Whale Scout
        Deep Green Wilderness
        Oceans Initiative
        Friends of Lime Kiln Society (FOLKS)
        The Whale Trail
        The Center for Whale Research
      

You can join Orcasound, too! Just read and e-sign the MOA.

Orcasound 2.0: new locations, node hardware/software, and app in 2018

New locations

Outreach/education nodes Extant: Seattle Aquarium listening kiosk; Port Townsend Marine Science Center listening station built with Killer Whale Tales and NOAA support 2018: Langley Whale Center Ocean Listening Exhibit; Lime Kiln Visitor Center audio/video kiosk (in progress) 2019: Integration of new live streaming tech and citizen science into extant educational nodes; Hydrophone nodes Current: Orcasound Lab Imminent: Bush Point; Port Townsend; Clallum Bay Planned in 2019: Eagle Point; MAST Center; Neah Bay?

New node hardware

Can we deploy a hydrophone node for less than $1000 (U.S.)?!

Basic ingredients (and costs):

($ 50) DIY hydrophone stand ($300 x2) Hydrophones -- dual for redundancy, stereo listening, and bearing determination ($125) Pisound board -- 192 kHz per channel, stereo ADC ($ 35) Raspberry Pi 3b+ -- single board computer (1.4GHz 64-bit quad-core processor) ($ 35) Audio cables and a waterproof box installed near the shoreline ($ 75) Power over Ethernet -- cable+splitters from the box to a 24-hour UPS and Internet router 60% of cost is hydrophones! (>75% for minimal research-grade) To make your own, see this detailed post about Orcasound's new hardware and software...

New node software

Can we stream using open-source software?

• ffmpeg and/or test-engine-engine-tools -- encode audio data (compressed stream and/or lossless archive)
• HTTP-based protocols:
  • HLS (HTTP Live Streaming, aimed at iOS/Safari) and
  • MPEG-DASH (Dynamic Adaptive Streaming over HTTP, for everything else)
• Current solution is HLS segments streamed via a video.js player and FLAC format for the archive (but long-term dream is FLAC within DASH)

And archive lossless data in the cloud?

• Amazon’s Simple Storage Services (S3) -- cost-effective data storage, public access, and automated management
• Upload script currently bash calling s3fs, but new python script will be optimized for live streaming and power/Internet outages
• FLAC files are currently 30 seconds long

Machine learning challenges (Nov 17 'Hack to give thanks')

• Bring citizen scientist real-time detections and classifications into live spectrogram display like Cornell's real-time bird call classifier
• Build training data set of killer whale signals for machine learning
1. Learn from expert's catalog of signals (~40 types of "pulsed calls")
2. Clip isolated, clear calls from continuous recordings of wild killer whales
• Discuss and implement architecture for machine learning
1. Review existing data architecture and flow
2. Adjust AWS security and set up hackathon participant access
3. Access FLAC audio data in AWS S3 buckets
4. Discuss and access EC2 Linux instance and other relevant services (e.g. Cloud9, Lambda?)
5. Build out Orcadata Git repository: a docker-based Python analysis environment, or ??
• Integrate machine learning approaches and products into Orcasound app
1. Orientation to the app, audio data scheme, and Orcasite Git repository (Backend is an Elixir app using the Phoenix framework. The Phoenix app serves a React app.)
2. Discuss how algorithmic and human detection (Activity button presses) and classification could work synergistically
3. Implement integration, including visualization and notifications (moderated or automated)