5 papers
Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity
Sebastian Ribecky, Jakob Abeßer, Hanna Lukashevich
In the context of music information retrieval, similarity-based approaches are useful for a variety of tasks that benefit from a query-by-example scenario. Music however, naturally…
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning
Jakob Abeßer, Meinard Müller
The deployment of machine listening algorithms in real-life applications is often impeded by a domain shift caused for instance by different microphone characteristics. In this pap…
USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios
Jakob Abeßer
This paper introduces a novel dataset for polyphonic sound event detection in urban sound monitoring use-cases. Based on isolated sounds taken from the FSD50k dataset, 20,000 polyp…
IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research
Jakob Abeßer, Saichand Gourishetti, András Kátai +3
In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematical…
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection
David S. Johnson, Wolfgang Lorenz, Michael Taenzer +4
Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed r…