9 papers
Restore, Separate, Restore: A Modular Framework for Music Source Restoration
Tobias Morocutti, Emmanouil Karystinaios, Gerhard Widmer
Music Source Restoration (MSR) seeks to recover original, unprocessed instrument stems from mixed, mastered, and possibly degraded recordings. Unlike conventional source separation…
RealDESED: A Real-World Domestic Sound Event Detection Benchmark
Florian Schmid, Paul Primus, Alexander Fichtinger +3
This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes. Each reco…
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…
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification
Tobias Morocutti, Florian Schmid, Khaled Koutini +1
Knowledge Distillation (KD) is a widespread technique for compressing the knowledge of large models into more compact and efficient models. KD has proved to be highly effective in…
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…
Effective Pre-Training of Audio Transformers for Sound Event Detection
Florian Schmid, Tobias Morocutti, Francesco Foscarin +3
We propose a pre-training pipeline for audio spectrogram transformers for frame-level sound event detection tasks. On top of common pre-training steps, we add a meticulously design…
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…
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation
Tobias Morocutti, Florian Schmid, Khaled Koutini +1
The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphone…