June 07. Video coming on TinyML Youtube channel.
Great coffee requires not just high quality coffee beans, but also a roasting process that consistently brings out the desired flavor and aroma. During the roasting the coffee beans will pop like popcorn ("cracking"), and the sound of these cracks is a good indicator of the development stage of the coffee. By using MEMS microphones and on-edge analysis using machine learning (TinyML), the Roest coffee roasters can use sound to automatically keep track of the roasting process. This technology has been developed in a collaboration between Roest and Soundsensing, and is shipping on Roest sample roaster since August 2020. In this talk you will hear about this fun and practical application of TinyML, and some of the challenges and solutions we found when deploying on-edge machine learning in professional grade electronics products.
Jon Nordby is a Machine Learning Engineer with 10 years of experience developing software for embedded systems and data processing of audio and images. He holds a Bachelor's degree in Electronics Engineering from 2010, and a Master's degree in Data Science from 2019. His specialization is machine learning for audio and sensor data. Since 2019 he is the Head of Machine Learning and Data Science at Soundsensing, a leading provider of IoT sensor systems using sound as the primary data source. Their systems are used for Noise Monitoring and Condition Monitoring of machinery. Jon is also the creator of emlearn, an open-source machine learning toolkit for microcontrollers and embedded devices.
Have an sensing/monitoring problem that can be approached with sound? In process-, manufacturing or other industries. Contact Soundsensing!