6 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…
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…
Online Single-Channel Audio-Based Sound Speed Estimation for Robust Multi-Channel Audio Control
Andreas Jonas Fuglsig, Mads Græsbøll Christensen, Jesper Rindom Jensen
Robust spatial audio control relies on accurate acoustic propagation models, yet environmental variations, especially changes in the speed of sound, cause systematic mismatches tha…
Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning
Monorama Swain, Bubai Maji, Jagabandhu Mishra +3
In this work, we address the challenge of building fair English ASR systems for second-language speakers. Our analysis of widely used ASR models, Whisper and Seamless-M4T, reveals…
Advances in Microphone Array Processing and Multichannel Speech Enhancement
Gongping Huang, Jesper R. Jensen, Jingdong Chen +5
This paper reviews pioneering works in microphone array processing and multichannel speech enhancement, highlighting historical achievements, technological evolution, commercializa…
A machine learning framework for acoustic reflector mapping
Usama Saqib, Letizia Marchegiani, Jesper Rindom Jensen
Sonar-based indoor mapping systems have been widely employed in robotics for several decades. While such systems are still the mainstream in underwater and pipe inspection settings…