AudSemThinker: Enhancing Audio-Language Models Through Reasoning over Semantics of Sound

Gijs Wijngaard, Elia Formisano, Michele Esposito, Michel Dumontier

Keywords: audio-language model, audio dataset, audio reasoning, semantics of sound

TL;DR: This paper introduces AudSemThinker, an audio-language model that reasons on the semantics of sound

Abstract: 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. In this paper, we present AudSemThinker, a model whose reasoning is structured around a framework of auditory semantics inspired by human cognition. To support this, we introduce AudSem, a novel dataset specifically curated for semantic descriptor reasoning in audio-language models. AudSem addresses the persistent challenge of data contamination in zero-shot evaluations by providing a carefully filtered collection of audio samples paired with captions generated through a robust multi-stage pipeline. Our experiments demonstrate that AudSemThinker outperforms state-of-the-art models across multiple training settings, highlighting its strength in semantic audio reasoning. Both AudSemThinker and the AudSem dataset are released publicly.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Flagged For Ethics Review: true

Submission Number: 27755