1 citations · 1 across the 13 of their papers we have counts for
15 papers
Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations
David Nadrchal, Monorama Swain, Florian Schmid +2
This work presents an automatic speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria rendering their speech uninte…
COSED: Setting the Bar for Open-Vocabulary Sound Event Detection
Florian Schmid, Sanjeel Parekh, Chi Ian Tang +5
Open-vocabulary Sound Event Detection detects and temporally localizes acoustic events described by arbitrary text queries. Progress in this emerging field is hard to assess: recen…
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…
Sound Event Detection with Boundary-Aware Optimization and Inference
Florian Schmid, Chi Ian Tang, Sanjeel Parekh +9
Temporal detection problems appear in many fields including time-series estimation, activity recognition and sound event detection (SED). In this work, we propose a new approach to…
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…
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
Florian Schmid, Paul Primus, Toni Heittola +3
This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge, along with its baseline system. Continuing the focus…
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…
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…
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…
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge
Florian Schmid, Paul Primus, Toni Heittola +4
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 c…
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…