First developed in 2020 by Google Summer of Code students Diego Rodríguez and Kunal Mehta, OrcaAL is an “active learning” tool. It lets you teach a machine learning model to recognize orca signals & observe how its performance evolves.

1. The machine presents you with audio samples that it finds confusing.
2. You help label the sounds, collaborating with a “crowd” of other human annotators.
3. After enough samples are labeled, the machine re-trains itself using the new data, then generates a new list of sounds that confuse it.
4. Repeat!

Open-source code

• Orca AL web app repo
• Orca AL SRKW call model research repo

Implementations

1. OrcaAL with Orcasound data and a SRKW call model

Presentations & products

Mar 8, 2022: DCLDE Oahu talk

Valentina Staneva pre-recorded a talk for the DCLDE 2022 session on “Software to enable DCLDE.” (DCLDE = Detection, Classification, Localization, and Density Estimation). Here is the slide deck:

Slide deck (PDF) for a 10-minute talk on “Active Listening & Learning for Efficient and Interactive Marine Sound Annotation and Classification”

The co-authors were: Kunal Mehta, Jose Giraldo, Diego Rodriguez, Jesse Lopez, Val Veirs, Scott Veirs, and Valentina Staneva.

Dec 2020: Meridian webinar on “Sound detection and classification with deep learning”

Collaborators

• Google Summer of Code students
  • Kunal Mehta (2020)
  • Diego Rodríguez (2020)
• Orcasound mentors (see also the Orcasound GSoC organization page)
  • Valentina Staneva (University of Washington, eScience Institute)
  • Jesse Lopez (Axiom Data Science)
  • Abhishek Singh (Google Summer of Code 2019 alum)
  • Dan Olsen (North Gulf Oceanic Society)
  • Hannah Meyers (University of Alaska)
  • Val Veirs (Beam Reach)
  • Scott Veirs (Beam Reach)

More info:

• AI for Orcas project page at Orcasound (including links to blog posts by GSoC students)