265 citations · 714 across the 33 of their papers we have counts for
8 papers · 2 filters
Domain-Incremental Learning for Audio Classification
Manjunath Mulimani, Annamaria Mesaros
In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a mode…
Class-Incremental Learning for Sound Event Localization and Detection
Ruchi Pandey, Manjunath Mulimani, Archontis Politis +1
This paper investigates the feasibility of class-incremental learning (CIL) for Sound Event Localization and Detection (SELD) tasks. The method features an incremental learner that…
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…
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations
Manjunath Mulimani, Annamaria Mesaros
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 m…
DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels
Samuele Cornell, Janek Ebbers, Constance Douwes +4
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-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…
Class-Incremental Learning for Multi-Label Audio Classification
Manjunath Mulimani, Annamaria Mesaros
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 i…