Acoustic Event Classification Using Low-Resolution Multi-Label Non-Negative Matrix Deconvolution

Acoustic event classification for monitoring applications is becoming feasible thanks to the increasing number of connected devices with a built-in microphone. The sound event classes are defined by annotating training data, which is a laborious process. Attempts have been made to reduce the workload on annotating the vast amounts of training data and are referred to as semi-supervised learning and active learning. In this paper we propose a non-negative matrix deconvolution (NMD) based approach, capable of modeling acoustic events from data labeled on a low-resolution and multi-label level and thereby reducing the annotation workload. We further show that the proposed extension of NMD is successfully applied for the classification of acoustic events, even in noisy conditions and with overlapping events.

Publication year:2018

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:0.1

CSS-citation score:1

Authors from:Government, Higher Education

Accessibility:Open