Sound events often occur in unstructured environments where they exhibit wide variations in
their frequency content and temporal structure. Convolutional neural networks (CNNs) are …

DCASE 2017 Challenge consists of four tasks: acoustic scene classification, detection of
rare sound events, sound event detection in real-life audio, and large-scale weakly …

We introduce TUT Acoustic Scenes 2016 database for environmental sound research,
consisting of binaural recordings from 15 different acoustic environments. A subset of this …

This paper presents and discusses various metrics proposed for evaluation of polyphonic
sound event detection systems used in realistic situations where there are typically multiple …

Sound event detection (SED) methods are tasked with labeling segments of audio
recordings by the presence of active sound sources. SED is typically posed as a supervised …

This article proposes an active learning system for sound event detection (SED). It aims at
maximizing the accuracy of a learned SED model with limited annotation effort. The …

Weakly labeled semi-supervised learning methods have recently drawn increasing attention
from the research community for sound event detection tasks. Due to the weakness of the …

In this article, a special decision surface for the weakly-supervised sound event detection
(SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are …

Sound event localization and detection (SELD) is a combined task that classifies acoustic
events from audio signals, estimates temporal boundaries, and identifies event locations …

The recent reports on population ageing in the most advanced countries are driving
governments and the scientific community to focus on technologies for providing assistance …