4 citations · 4 across the 4 of their papers we have counts for
6 papers
VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +7
Expressive speech synthesis has outpaced expressive speech perception: systems now render fine-grained vocal performances that no public benchmark can score. Most benchmarks for th…
AudioToolAgent: An Agentic Framework for Audio-Language Models
Gijs Wijngaard, Elia Formisano, Michel Dumontier +1
Large Audio-Language Models (LALMs) perform well on audio understanding tasks but lack multistep reasoning and tool-calling found in recent Large Language Models (LLMs). This paper…
Data-Balanced Curriculum Learning for Audio Question Answering
Gijs Wijngaard, Elia Formisano, Michele Esposito +1
Audio question answering (AQA) requires models to understand acoustic content and perform complex reasoning. Current models struggle with dataset imbalances and unstable training d…
AudSemThinker: Enhancing Audio-Language Models through Reasoning over Semantics of Sound
Gijs Wijngaard, Elia Formisano, Michele Esposito +1
Audio-language models have shown promising results in various sound understanding tasks, yet they remain limited in their ability to reason over the fine-grained semantics of sound…
Audio-Language Datasets of Scenes and Events: A Survey
Gijs Wijngaard, Elia Formisano, Michele Esposito +1
Audio-language models (ALMs) generate linguistic descriptions of sound-producing events and scenes. Advances in dataset creation and computational power have led to significant pro…
ACES: Evaluating Automated Audio Captioning Models on the Semantics of Sounds
Gijs Wijngaard, Elia Formisano, Bruno L. Giordano +1
Automated Audio Captioning is a multimodal task that aims to convert audio content into natural language. The assessment of audio captioning systems is typically based on quantitat…