Sound Event Classification and Detection with Weakly Labeled Data

Abstract

The Sound Event Classification (SEC) task involves recognizing the set of active sound events in an audio recording. The Sound Event Detection (SED) task involves, in addition to SEC, detecting the temporal onset and offset of every sound event in an audio recording. Generally, SEC and SED are treated as supervised classification tasks that require labeled datasets. SEC only requires weak labels, i.e., annotation of active sound events, without the temporal information, whereas SED requires strong labels, i.e., annotation of the onset and offset times of every sound event, which makes annotation for SED more tedious than for SEC. In this paper, we propose two methods for joint SEC and SED using weakly labeled data: a Fully Convolutional Network (FCN) and a novel method that combines a Convolutional Neural Network with an attention layer (CNNatt). Unlike most prior work, the proposed methods do not assume that the weak labels are active during the entire recording and can scale to large datasets. We report state-of-the-art SEC results obtained with the largest weakly labeled dataset - Audioset