Semi-automated Annotation of Audible Home Activities

Abstract

Data annotation is the process of segmenting and labelling any type of data (images, audio or text). It is an important task for producing reliable datasets that can be used to train machine learning algorithms for the purpose of Activity Recognition. This paper presents the work in progress towards a semi-automated approach for collecting and annotating audio data from simple sounds that are typically produced at home when people perform daily activities, for example the sound of running water when a tap is open. We propose the use of an app called ISSA (Intelligent System for Sound Annotation) running on smart microphones to facilitate the semi-automated annotation of audible activities. When a sound is produced, the app tries to classify the activity and notifies the user, who can correct the classification and/or provide additional information such as the location of the sound. To illustrate the feasibility of the approach, an initial version of ISSA was implemented to train an audio classifier in a one-bedroom apartment.

Publication
In Proceedings - ARDUOUS - 3rd International Workshop on Annotation of useR Data for UbiquitOUs Systems. 17th IEEE International Conference on Pervasive Computing and Communications (PerCom2019)
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Matias Garcia-Constantino
Lecturer in Computing Science

My research interests include Data Analysis, Internet of Things (IoT), Artificial Intelligence, Natural Language Processing and Network Science. matter.