5 citations · 8 across the 12 of their papers we have counts for
12 papers
Local Density-Based Anomaly Score Normalization for Domain Generalization
Kevin Wilkinghoff, Haici Yang, Janek Ebbers +3
State-of-the-art anomalous sound detection (ASD) systems in domain-shifted conditions rely on projecting audio signals into an embedding space and using distance-based outlier dete…
UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing
Yung-Hsuan Lai, Janek Ebbers, Yu-Chiang Frank Wang +3
Audio-Visual Video Parsing (AVVP) entails the challenging task of localizing both uni-modal events (i.e., those occurring exclusively in either the visual or acoustic modality of a…
Task-Aware Unified Source Separation
Kohei Saijo, Janek Ebbers, François G. Germain +2
Several attempts have been made to handle multiple source separation tasks such as speech enhancement, speech separation, sound event separation, music source separation (MSS), or…
Leveraging Audio-Only Data for Text-Queried Target Sound Extraction
Kohei Saijo, Janek Ebbers, François G. Germain +3
The goal of text-queried target sound extraction (TSE) is to extract from a mixture a sound source specified with a natural-language caption. While it is preferable to have access…
DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels
Samuele Cornell, Janek Ebbers, Constance Douwes +4
The Detection and Classification of Acoustic Scenes and Events Challenge Task 4 aims to advance sound event detection (SED) systems in domestic environments by leveraging training…
Sound Event Bounding Boxes
Janek Ebbers, Francois G. Germain, Gordon Wichern +1
Sound event detection is the task of recognizing sounds and determining their extent (onset/offset times) within an audio clip. Existing systems commonly predict sound presence con…
Post-Processing Independent Evaluation of Sound Event Detection Systems
Janek Ebbers, Reinhold Haeb-Umbach, Romain Serizel
Due to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating mode. Therefor…
Investigation into Target Speaking Rate Adaptation for Voice Conversion
Michael Kuhlmann, Fritz Seebauer, Janek Ebbers +2
Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a…
Threshold Independent Evaluation of Sound Event Detection Scores
Janek Ebbers, Romain Serizel, Reinhold Haeb-Umbach
Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detect…
Voice Conversion Based Speaker Normalization for Acoustic Unit Discovery
Thomas Glarner, Janek Ebbers, Reinhold Häb-Umbach
Discovering speaker independent acoustic units purely from spoken input is known to be a hard problem. In this work we propose an unsupervised speaker normalization technique prior…
Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection
Janek Ebbers, Reinhold Haeb-Umbach
In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domest…
Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations
Janek Ebbers, Michael Kuhlmann, Tobias Cord-Landwehr +1
In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder a…