Zobrazeno 1 - 10
of 405
pro vyhledávání: '"Schmid, Florian A."'
Autor:
Schmid, Florian, Morocutti, Tobias, Foscarin, Francesco, Schlüter, Jan, Primus, Paul, Widmer, Gerhard
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 designed training routine on AudioSet frame-level annotations. This includes
Externí odkaz:
http://arxiv.org/abs/2409.09546
Dual-encoder-based audio retrieval systems are commonly optimized with contrastive learning on a set of matching and mismatching audio-caption pairs. This leads to a shared embedding space in which corresponding items from the two modalities end up c
Externí odkaz:
http://arxiv.org/abs/2408.11641
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 sound concepts through voice, QBV offers the more intuitive and convenien
Externí odkaz:
http://arxiv.org/abs/2408.11638
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 Challenge. We fine-tune three large Audio Spectrogram Transformers, PaSS
Externí odkaz:
http://arxiv.org/abs/2408.00791
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 2024 challenge proposes learning from two heterogeneous datasets, includi
Externí odkaz:
http://arxiv.org/abs/2407.12997
Autor:
Schmid, Florian, Primus, Paul, Heittola, Toni, Mesaros, Annamaria, Martín-Morató, Irene, Koutini, Khaled, Widmer, Gerhard
This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on rec
Externí odkaz:
http://arxiv.org/abs/2405.10018
Autor:
Pietanesi, Laura, Marganska, Magdalena, Mayer, Thomas, Barth, Michael, Chen, Lin, Zou, Ji, Weindl, Adrian, Liebig, Alexander, Díaz-Pardo, Rebeca, Suri, Dhavala, Schmid, Florian, Gießibl, Franz J., Richter, Klaus, Tserkovnyak, Yaroslav, Kronseder, Matthias, Back, Christian H.
Publikováno v:
Phys. Rev. B 109, 064424 (2024)
Ferromagnetic resonance is used to reveal features of the buried electronic band structure at interfaces between ferromagnetic metals and topological insulators. By monitoring the evolution of magnetic damping, the application of this method to a hyb
Externí odkaz:
http://arxiv.org/abs/2403.03518
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 architecture for many tasks. Audio Spectrogram Transformers are excel
Externí odkaz:
http://arxiv.org/abs/2310.15648
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 microphones introduce distributional shifts in the digitized audio signals due t
Externí odkaz:
http://arxiv.org/abs/2305.07499
Solving tasks such as speaker recognition, music classification, or semantic audio event tagging with deep learning models typically requires computationally demanding networks. General-purpose audio embeddings (GPAEs) are dense representations of au
Externí odkaz:
http://arxiv.org/abs/2303.01879