8 papers
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…
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…
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…