265 citations · 714 across the 33 of their papers we have counts for
36 papers
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…
Sampling Bias Compensation for Robust Evaluation of Audio Classification Systems with Partially Labeled Evaluation Datasets
Javier Naranjo-Alcazar, Annamaria Mesaros, Tuomas Virtanen +1
The performance of acoustic machine learning systems is commonly evaluated using fully annotated test sets. In real-world deployments, however, exhaustively labeling large volumes…
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…