aiqbench analysis
This research applies fine-tuned foundation models like CLAP to address auditory hypersensitivity in autistic individuals, demonstrating how adapting large-scale audio pretraining can generalize affective responses from limited annotated data. By leveraging low-rank adaptation techniques, the study offers a targeted approach to improving emotion recognition systems for atypical listening profiles rather than relying on standard neurotypical datasets.
Audio emotion recognition for atypical hearing
arXiv:2609.31168v1 Announce Type: new Abstract: My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivity in people with autism, a phenomenon that is often difficult to evaluate and unique to each individual. Our core idea is to leverage our understanding of affect from acoustic traits, relying on the possibility of generalizing affective responses from a small amount of annotated data. As a first step, we fine-tune a large foundation model, Contrastive Language-Audio Pretraining (CLAP) using low-rank adaptation (LoRA), trained on a valence and arousal dataset of neurotypical listeners.
Abstract:My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivity in people with autism, a phenomenon that is often difficult to evaluate and unique to each individual. Our core idea is to leverage our understanding of affect from acoustic traits, relying on the possibility of generalizing affective responses from a small amount of annotated data. As a first step, we fine-tune a large foundation model, Contrastive Language-Audio Pretraining (CLAP) using low-rank adaptation (LoRA), trained on a valence and arousal dataset of neurotypical listeners.
Machine Learning (cs.LG)
arXiv:2609.31168 [cs.LG]
(or arXiv:2609.31168v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.31168 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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