Acoustic environments affect acoustic characteristics of sound to be recognized by
physically interacting with sound wave propagation. Thus, training acoustic models for audio …

Performing an adequate evaluation of sound event detection (SED) systems is far from trivial
and is still subject to ongoing research. The recently proposed polyphonic sound detection …

This paper proposes a benchmark of submissions to Detection and Classification Acoustic
Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of …

We present a novel approach to automatically detect and classify great ape calls from
continuous raw audio recordings collected during field research. Our method leverages …

Jointly learning from a small labeled set and a larger unlabeled set is an active research
topic under semi-supervised learning (SSL). In this paper, we propose a novel SSL method …

Sound event detection (SED) entails identifying the type of sound and estimating its
temporal boundaries from acoustic signals. These events are uniquely characterized by their …

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task
4 is to evaluate systems for the detection of sound events in domestic environments using an …

Sound event detection is the task of recognizing sounds and determining their extent
(onset/offset times) within an audio clip. Existing systems commonly predict sound presence …

Since diarization and source separation of meeting data are closely related tasks, we here
propose an approach to perform the two objectives jointly. It builds upon the target-speaker …

Recent state-of-the-art (SOTA) semi-supervised learning methods have shown great
promise in improving sound event detection (SED) performance when the labeled data is …