Zobrazeno 1 - 10
of 3 045
pro vyhledávání: '"Mesaros, A."'
This paper introduces briefly the history and growth of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, workshop, research area and research community. Created in 2013 as a data evaluation challenge, DCASE has become
Externí odkaz:
http://arxiv.org/abs/2410.04951
Autor:
Triantafyllopoulos, Andreas, Tsangko, Iosif, Gebhard, Alexander, Mesaros, Annamaria, Virtanen, Tuomas, Schuller, Björn
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand sounds. They feature several advantages over traditional pipelines: amon
Externí odkaz:
http://arxiv.org/abs/2407.15672
The spatial structure of in-gap Yu-Shiba-Rusinov (YSR) bound states induced by a magnetic impurity in a superconductor is the essential ingredient for the possibility of engineering collective impurity states. Recently, a saddle-point approximation [
Externí odkaz:
http://arxiv.org/abs/2406.14323
In this paper, we propose a method for online domain-incremental learning of acoustic scene classification from a sequence of different locations. Simply training a deep learning model on a sequence of different locations leads to forgetting of previ
Externí odkaz:
http://arxiv.org/abs/2406.13386
Autor:
Cornell, Samuele, Ebbers, Janek, Douwes, Constance, Martín-Morató, Irene, Harju, Manu, Mesaros, Annamaria, Serizel, Romain
The Detection and Classification of Acoustic Scenes and Events Challenge Task 4 aims to advance sound event detection (SED) systems in domestic environments by leveraging training data with different supervision uncertainty. Participants are challeng
Externí odkaz:
http://arxiv.org/abs/2406.08056
Autor:
Schmid, Florian, Primus, Paul, Heittola, Toni, Mesaros, Annamaria, Martín-Morató, Irene, Koutini, Khaled, Widmer, Gerhard
This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on rec
Externí odkaz:
http://arxiv.org/abs/2405.10018
Publikováno v:
Nature Communications 15, 3774 (2024)
Structural distortions and imperfections are a crucial aspect of materials science, on the macroscopic scale providing strength, but also enhancing corrosion and reducing electrical and thermal conductivity. At the nanometre scale, multi-atom imperfe
Externí odkaz:
http://arxiv.org/abs/2405.03748
Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA). While this task has numerous applications and has been extensively researched in recent years, it fails t
Externí odkaz:
http://arxiv.org/abs/2403.11827
In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets. The key step in AVC is to randomly sample audio and video clips from the dataset and learn to
Externí odkaz:
http://arxiv.org/abs/2402.02899
In this paper, we propose a method for class-incremental learning of potentially overlapping sounds for solving a sequence of multi-label audio classification tasks. We design an incremental learner that learns new classes independently of the old cl
Externí odkaz:
http://arxiv.org/abs/2401.04447