3 citations · 7 across the 17 of their papers we have counts for
17 papers
VCNAC: A Variable-Channel Neural Audio Codec for Mono, Stereo, and Surround Sound
Florian Grötschla, Arunasish Sen, Alessandro Lombardi +2
We present VCNAC, a variable channel neural audio codec. Our approach features a single encoder and decoder parametrization that enables native inference for different channel setu…
Multi-bit Audio Watermarking
Luca A. Lanzendörfer, Kyle Fearne, Florian Grötschla +1
We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given…
Bias beyond Borders: Global Inequalities in AI-Generated Music
Ahmet Solak, Florian Grötschla, Luca A. Lanzendörfer +1
While recent years have seen remarkable progress in music generation models, research on their biases across countries, languages, cultures, and musical genres remains underexplore…
SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos
Amir Dellali, Luca A. Lanzendörfer, Florian Grötschla +1
We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content. Our approach…
Benchmarking Diarization Models
Luca A. Lanzendörfer, Florian Grötschla, Cesare Blaser +1
Speaker diarization is the task of partitioning audio into segments according to speaker identity, answering the question of "who spoke when" in multi-speaker conversation recordin…
Benchmarking Music Generation Models and Metrics via Human Preference Studies
Florian Grötschla, Ahmet Solak, Luca A. Lanzendörfer +1
Recent advancements have brought generated music closer to human-created compositions, yet evaluating these models remains challenging. While human preference is the gold standard…
High-Fidelity Music Vocoder using Neural Audio Codecs
Luca A. Lanzendörfer, Florian Grötschla, Michael Ungersböck +1
While neural vocoders have made significant progress in high-fidelity speech synthesis, their application on polyphonic music has remained underexplored. In this work, we propose D…
Audio Atlas: Visualizing and Exploring Audio Datasets
Luca A. Lanzendörfer, Florian Grötschla, Uzeyir Valizada +1
We introduce Audio Atlas, an interactive web application for visualizing audio data using text-audio embeddings. Audio Atlas is designed to facilitate the exploration and analysis…
Benchmarking Positional Encodings for GNNs and Graph Transformers
Florian Grötschla, Jiaqing Xie, Roger Wattenhofer
Positional Encodings (PEs) are essential for injecting structural information into Graph Neural Networks (GNNs), particularly Graph Transformers, yet their empirical impact remains…
SNAC: Multi-Scale Neural Audio Codec
Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer
Neural audio codecs have recently gained popularity because they can represent audio signals with high fidelity at very low bitrates, making it feasible to use language modeling ap…
On the Lifecycle of a Lightning Network Payment Channel
Florian Grötschla, Lioba Heimbach, Severin Richner +1
The Bitcoin Lightning Network, launched in 2018, serves as a layer 2 scaling solution for Bitcoin. The Lightning Network allows users to establish channels between each other and s…
Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
Florian Grötschla, Luca Strässle, Luca A. Lanzendörfer +1
Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable i…
GraphFSA: A Finite State Automaton Framework for Algorithmic Learning on Graphs
Florian Grötschla, Joël Mathys, Christoffer Raun +1
Many graph algorithms can be viewed as sets of rules that are iteratively applied, with the number of iterations dependent on the size and complexity of the input graph. Existing m…
AEye: A Visualization Tool for Image Datasets
Florian Grötschla, Luca A. Lanzendörfer, Marco Calzavara +1
Image datasets serve as the foundation for machine learning models in computer vision, significantly influencing model capabilities, performance, and biases alongside architectural…
Benchmarking GNNs Using Lightning Network Data
Rainer Feichtinger, Florian Grötschla, Lioba Heimbach +1
The Bitcoin Lightning Network is a layer 2 protocol designed to facilitate fast and inexpensive Bitcoin transactions. It operates by establishing channels between users, where Bitc…
Next Level Message-Passing with Hierarchical Support Graphs
Carlos Vonessen, Florian Grötschla, Roger Wattenhofer
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being…
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Stefan Künzli, Florian Grötschla, Joël Mathys +1
Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equati…