7 citations · 15 across the 6 of their papers we have counts for
7 papers · 1 filter
TACOS: Temporally-aligned Audio CaptiOnS for Language-Audio Pretraining
Paul Primus, Florian Schmid, Gerhard Widmer
Learning to associate audio with textual descriptions is valuable for a range of tasks, including pretraining, zero-shot classification, audio retrieval, audio captioning, and text…
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…
Estimated Audio-Caption Correspondences Improve Language-Based Audio Retrieval
Paul Primus, Florian Schmid, Gerhard Widmer
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 embeddi…
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…
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples
Paul Primus, Verena Haunschmid, Patrick Praher +1
Unsupervised anomalous sound detection is concerned with identifying sounds that deviate from what is defined as 'normal', without explicitly specifying the types of anomalies. A s…
Receptive-Field Regularized CNNs for Music Classification and Tagging
Khaled Koutini, Hamid Eghbal-Zadeh, Verena Haunschmid +3
Convolutional Neural Networks (CNNs) have been successfully used in various Music Information Retrieval (MIR) tasks, both as end-to-end models and as feature extractors for more co…