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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…
On Temporal Binding in Large Audio Language Models
Paul Primus, Gerhard Widmer
Reasoning about temporal structure of audio recordings requires Large Audio Language Models (LALMs) to associate sound events with their temporal position. Understanding the underl…
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…
Music Boomerang: Reusing Diffusion Models for Data Augmentation and Audio Manipulation
Alexander Fichtinger, Jan Schlüter, Gerhard Widmer
Generative models of music audio are typically used to generate output based solely on a text prompt or melody. Boomerang sampling, recently proposed for the image domain, allows g…
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…
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models
Florian Schmid, Khaled Koutini, Gerhard Widmer
The introduction of large-scale audio datasets, such as AudioSet, paved the way for Transformers to conquer the audio domain and replace CNNs as the state-of-the-art neural network…