Weakly-Supervised Classification of Domestic Acoustic Events for Indoor Monitoring Applications

Details

12:25 - 12:38 | Mon 5 Mar | Treasure Island E | MoBT2.6

Session: BHI Special Session # 2 – Intelligent Wellness Sensing

Abstract

This abstract discusses a non-negative matrix deconvolution (NMD) based learning algorithm capable of identifying acoustic events from weakly supervised (WS) data. The weakly supervision is implemented by indicating the events that took place over a longer period of time without identifying beginning nor endings. We will show that WS-NMD is successfully applied for the task of acoustic event classification and that it achieves similar results compared to the baselines on the NAR-dataset but with significant less annotation work.