6 papers
Low-Latency Neural Models for Real-Time Music Enhancement
Emmanouil Karystinaios, Jonathan Greif, David Nadrchal +2
Music recordings and live streams are often affected by noise, reverberation, spectral imbalances, or artifacts that degrade listening quality. While speech enhancement has matured…
Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN
Tobias Morocutti, Emmanouil Karystinaios, Jonathan Greif +1
Music Source Restoration (MSR) targets recovery of original, unprocessed instrument stems from fully mixed and mastered audio, where production effects and distribution artifacts v…
Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models
Tobias Morocutti, Florian Schmid, Jonathan Greif +2
We target the problem of developing new low-complexity networks for the sound event detection task. Our goal is to meticulously analyze the performance-complexity trade-off, aiming…
Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining
Jonathan Greif, Florian Schmid, Paul Primus +1
Query-by-Vocal Imitation (QBV) is about searching audio files within databases using vocal imitations created by the user's voice. Since most humans can effectively communicate sou…
Improving Audio Spectrogram Transformers for Sound Event Detection Through Multi-Stage Training
Florian Schmid, Paul Primus, Tobias Morocutti +2
This technical report describes the CP-JKU team's submission for Task 4 Sound Event Detection with Heterogeneous Training Datasets and Potentially Missing Labels of the DCASE 24 Ch…
Multi-Iteration Multi-Stage Fine-Tuning of Transformers for Sound Event Detection with Heterogeneous Datasets
Florian Schmid, Paul Primus, Tobias Morocutti +2
A central problem in building effective sound event detection systems is the lack of high-quality, strongly annotated sound event datasets. For this reason, Task 4 of the DCASE 202…