3 citations · 5 across the 8 of their papers we have counts for
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…
Evaluating the Temporal Detection Capability of Integrated Gradients Applied on Sound Classifier
Martynas Dumpis, Tuomas Virtanen
Gradient-based attribution methods can highlight input regions important for neural network predictions, but their effectiveness for temporal sound event detection in audio classif…
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…
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time
John Martinsson, Tuomas Virtanen, Maria Sandsten +1
Accurate labels are critical for deriving robust machine learning models. Labels are used to train supervised learning models and to evaluate most machine learning paradigms. In th…
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…
Noise-to-mask Ratio Loss for Deep Neural Network based Audio Watermarking
Martin Moritz, Toni Olán, Tuomas Virtanen
Digital audio watermarking consists in inserting a message into audio signals in a transparent way and can be used to allow automatic recognition of audio material and management o…
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…