265 citations · 714 across the 33 of their papers we have counts for
31 papers · 1 filter
Domain-Incremental Learning for Generative Speech Enhancement
Manjunath Mulimani, Annamaria Mesaros, Minje Kim +1
We propose a domain-incremental learning framework for generative speech enhancement (SE) that learns from a sequence of datasets or domains recorded under diverse acoustic conditi…
Investigating catastrophic forgetting in sound event classification
Riccardo Casciotti, Annamaria Mesaros
This work investigates a number of approaches to prevent catastrophic forgetting in class incremental learning scenarios for sound event classification tasks. We analyze the proble…
Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task
Riccardo Casciotti, Manjunath Mulimani, Manu Harju +2
This paper presents the Domain-Agnostic Incremental Learning for Audio Classification Task of the DCASE 2026 Challenge. Incremental learning refers to sequentially learning new tas…
Incremental learning for audio classification with Hebbian Deep Neural Networks
Riccardo Casciotti, Francesco De Santis, Alberto Antonietti +1
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologi…
Online incremental learning for audio classification using a pretrained audio model
Manjunath Mulimani, Annamaria Mesaros
Incremental learning aims to learn new tasks sequentially without forgetting the previously learned ones. Most of the existing incremental learning methods for audio focus on train…
Sound event detection with audio-text models and heterogeneous temporal annotations
Manu Harju, Annamaria Mesaros
Recent advances in generating synthetic captions based on audio and related metadata allow using the information contained in natural language as input for other audio tasks. In th…
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…
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…
Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall
Manu Harju, Annamaria Mesaros
Classification systems are normally trained by minimizing the cross-entropy between system outputs and reference labels, which makes the Kullback-Leibler divergence a natural choic…
Training sound event detection with soft labels from crowdsourced annotations
Irene Martín-Morató, Manu Harju, Paul Ahokas +1
In this paper, we study the use of soft labels to train a system for sound event detection (SED). Soft labels can result from annotations which account for human uncertainty about…
Incremental Learning of Acoustic Scenes and Sound Events
Manjunath Mulimani, Annamaria Mesaros
In this paper, we propose a method for incremental learning of two distinct tasks over time: acoustic scene classification (ASC) and audio tagging (AT). We use a simple convolution…
Self-supervised learning of audio representations using angular contrastive loss
Shanshan Wang, Soumya Tripathy, Annamaria Mesaros
In Self-Supervised Learning (SSL), various pretext tasks are designed for learning feature representations through contrastive loss. However, previous studies have shown that this…
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…
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
Shanshan Wang, Archontis Politis, Annamaria Mesaros +1
Learning from audio-visual data offers many possibilities to express correspondence between the audio and visual content, similar to the human perception that relates aural and vis…
Crowdsourcing strong labels for sound event detection
Irene Martín-Morató, Manu Harju, Annamaria Mesaros
Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task. We present a…
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…