3 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Automatic Contextual Audio Denoising
Diep Luong, Konstantinos Drossos, Mikko Heikkinen +1
Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in…
Computer Audition: From Task-Specific Machine Learning to Foundation Models
Andreas Triantafyllopoulos, Iosif Tsangko, Alexander Gebhard +3
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand so…
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning
John Martinsson, Olof Mogren, Maria Sandsten +1
We propose an adaptive change point detection method (A-CPD) for machine guided weak label annotation of audio recording segments. The goal is to maximize the amount of information…