USING OPEN SOURCE NEURAL NETWORKS FOR NOISE CLASSIFICATION IN PRODUCTION AS NOVICE TO MACHINE LEARNING: SETTING FOOT IN THE WORLD OF MACHINE LEARNING AS COMPANY WITHOUT PRIOR KNOWLEDGE

Abstract

An overwhelming amount of data processing is being taken over by machine learning. These algorithms are either open source academic works or closed off proprietary products. Especially the field of noise classification for noise monitoring is of great interest to both profit oriented noise management industry and academic psycho-acoustic research. In order to soften up this dichotomy of "open" and "closed", it shall be investigated how well a company without any prior machine learning knowledge can set foot in the world of machine learning using only open access methods to create a usable product. To combine the spirits of corporate interest and open source, this paper aims to provide the findings of audio and acoustics measurement company NTi-Audio AG on the quest for a universal noise classification system for their noise monitoring solutions. The road taken shows how an open source pre-trained convolutional neural network can be combined with post-processing to achieve noise classification all around the world, while also showing the dead-ends developers without any training in machine learning run into.