Features
- Audio data vectorisation
- Real-time reporting interface
- Machine learning classifier
De Beers Group
Machine Learning Classifier
Using hydrophone audio data to classify soil composition.
Main Takeaway
De Beers Group made proprietory hydrophone audio data available to a group of hackathon contenders which the Codeswop team formed part of. Our challenge was to assist in identifying soil composition based purely on drilling audio date which is available in real-time.
Technology and Integrations
SciKit Learn, Pandas, NumPy, Matplotlib, Seaborn, TensorFlow, Keras, MongoDB, Docker, Gitlab
Team Members
Dean Harber - Lead Architect
Tiaan Hendricks - Data Engineer
Regardt Nel - Audio Engineer
Service
Software Architecture and Development
Machine Learning Environment Development
Time to Launch
1 Week
Outcomes
Web application built with Meteor and React, MongoDB for Data, Scikit Learn for Machine Learning, Docker for containerization, and Gitlab for CI/CD.
