75 citations · 394 across the 149 of their papers we have counts for
13 papers · 1 filter
Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
Leo Schmidt-Traub, Frédéric Berdoz, Luca A. Lanzendörfer +1
Neural audio codecs based on Residual Vector Quantization (RVQ) have become the dominant discrete representation for token-based general audio generation, yet resynthesizing high-q…
SAO-Instruct: Free-form Audio Editing using Natural Language Instructions
Michael Ungersböck, Florian Grötschla, Luca A. Lanzendörfer +3
Generative models have made significant progress in synthesizing high-fidelity audio from short textual descriptions. However, editing existing audio using natural language has rem…
High-Fidelity Speech Enhancement via Discrete Audio Tokens
Luca A. Lanzendörfer, Frédéric Berdoz, Antonis Asonitis +1
Recent autoregressive transformer-based speech enhancement (SE) methods have shown promising results by leveraging advanced semantic understanding and contextual modeling of speech…
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…
MaskBeat: Loopable Drum Beat Generation
Luca A. Lanzendörfer, Florian Grötschla, Karim Galal +1
We present MaskBeat, a transformer-based approach for loopable drum pattern generation. Rather than predicting drum hits sequentially, our method uses bidirectional attention with…
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…
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…
Siamese SIREN: Audio Compression with Implicit Neural Representations
Luca A. Lanzendörfer, Roger Wattenhofer
Implicit Neural Representations (INRs) have emerged as a promising method for representing diverse data modalities, including 3D shapes, images, and audio. While recent research ha…
DISCO-10M: A Large-Scale Music Dataset
Luca A. Lanzendörfer, Florian Grötschla, Emil Funke +1
Music datasets play a crucial role in advancing research in machine learning for music. However, existing music datasets suffer from limited size, accessibility, and lack of audio…