625 citations · 1.4k across the 18 of their papers we have counts for
19 papers
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…
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…
Multi-label Zero-Shot Audio Classification with Temporal Attention
Duygu Dogan, Huang Xie, Toni Heittola +1
Zero-shot learning models are capable of classifying new classes by transferring knowledge from the seen classes using auxiliary information. While most of the existing zero-shot l…
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…
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…
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…
Zero-Shot Audio Classification using Image Embeddings
Duygu Dogan, Huang Xie, Toni Heittola +1
Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typica…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä +2
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detect…
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Emre Çakır, Giambattista Parascandolo, Toni Heittola +2
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are…