An unsupervised noise classification smartphone app for hearing improvement devices
Nasim Alamdari, F. Saki, Abhishek Sehgal, Nasser Kehtarnavaz · 2017
This paper presents an app for running a previously developed unsupervised noise classifier in realtime on smartphone/tablet platforms. The steps taken to enable the development of this app are discussed. The app is utilized to carry out field testing of the unsupervised classification of actual encountered noise environments without any prior training and without specifying the number of noise classes or clusters. Two objective measures of cluster purity and normalized mutual information are considered to examine the performance of the app in the field with the user acting as the identifier of the ground truth classes. The results obtained indicate the effectiveness of this real-time smartphone app for carrying out the environmental noise classification in an unsupervised manner.