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20172025
most citedConvolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

625 citations · 1.4k across the 18 of their papers we have counts for

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15 papers · 1 filter

eess.AS2025

Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge

Florian Schmid, Paul Primus, Toni Heittola +3

This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge, along with its baseline system. Continuing the focus…

eess.AS2024

A decade of DCASE: Achievements, practices, evaluations and future challenges

Annamaria Mesaros, Romain Serizel, Toni Heittola +2

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 commu…

eess.AS2024

Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge

Florian Schmid, Paul Primus, Toni Heittola +4

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 c…

eess.AS2024

Positive and negative sampling strategies for self-supervised learning on audio-video data

Shanshan Wang, Soumya Tripathy, Toni Heittola +1

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…

eess.AS2022★ 22 cited

Low-complexity acoustic scene classification in DCASE 2022 Challenge

Irene Martín-Morató, Francesco Paissan, Alberto Ancilotto +5

This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 2022 Challenge. The task was a continuation from the previous years, but the low-c…

eess.AS2021★ 265 cited

Sound Event Detection: A Tutorial

Annamaria Mesaros, Toni Heittola, Tuomas Virtanen +1

The goal of automatic sound event detection (SED) methods is to recognize what is happening in an audio signal and when it is happening. In practice, the goal is to recognize at wh…

eess.AS2021★ 32 cited

Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems

Irene Martín-Morató, Toni Heittola, Annamaria Mesaros +1

This paper presents the details of Task 1A Acoustic Scene Classification in the DCASE 2021 Challenge. The task targeted development of low-complexity solutions with good generaliza…

eess.AS2021★ 5 cited

Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions

Shanshan Wang, Toni Heittola, Annamaria Mesaros +1

This paper presents the details of the Audio-Visual Scene Classification task in the DCASE 2021 Challenge (Task 1 Subtask B). The task is concerned with classification using audio…

eess.AS2020★ 3 cited

A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis

Shanshan Wang, Annamaria Mesaros, Toni Heittola +1

This paper introduces a curated dataset of urban scenes for audio-visual scene analysis which consists of carefully selected and recorded material. The data was recorded in multipl…

eess.AS2020★ 139 cited

Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019

Archontis Politis, Annamaria Mesaros, Sharath Adavanne +2

Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal acti…

eess.AS2020★ 40 cited

Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions

Toni Heittola, Annamaria Mesaros, Tuomas Virtanen

This paper presents the details of Task 1: Acoustic Scene Classification in the DCASE 2020 Challenge. The task consists of two subtasks: classification of data from multiple device…

eess.AS2020★ 1 cited

Active Learning for Sound Event Detection

Shuyang Zhao, Toni Heittola, Tuomas Virtanen

This paper 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 propose…

eess.AS2019

City classification from multiple real-world sound scenes

Helen L. Bear, Toni Heittola, Annamaria Mesaros +2

The majority of sound scene analysis work focuses on one of two clearly defined tasks: acoustic scene classification or sound event detection. Whilst this separation of tasks is us…

eess.AS2018

Acoustic Scene Classification: A Competition Review

Shayan Gharib, Honain Derrar, Daisuke Niizumi +5

In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. We des…

eess.AS2018★ 180 cited

A multi-device dataset for urban acoustic scene classification

Annamaria Mesaros, Toni Heittola, Tuomas Virtanen

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the perform…