How are machine learning models built from the ground up? From collecting data and building datasets, all the way to to building advanced, AI-powered predictive models - this internship provides you with the infrastructure and personal mentorship to really get your feet wet in the field of ML/Data Science. We are sound-centric, and use acoustics as our data source. Check this presentation from Soundsensing CTO Jon Nordby (and your would-be mentor) for more in-depth info: https://www.youtube.com/watch?v=QEEBNF0aeeg Ex tasks with varying levels of abstraction: - Collecting sound data in the wild - Finding and applying existing technology and research - Performing feasibility studies - Building ML models for specific applications
Ex ML use-cases we're working on right now:
- Audio Classification. Identify what things are happening in a sound clip
- Event Detection. Find the precise times that a specific sound occurs
- Anomaly Detection. Identify when out-of-the-ordinary things occur
- Data Presentation. How to present information to user in an effective manner
Soundsensing har utviklet en IoT lydsensor som måler, identifiserer, og loggfører lydkilder ved hjelp av maskinlæring.
Ole Johan A. Bjerke