Audio emotion recognition for atypical hearing
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Abstract (arXiv)
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.
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References · 13
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
- 2022Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentationcited by 719
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 486
- 2022HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detectioncited by 176
- 2020Zero-Shot Audio Classification via Semantic Embeddingscited by 21
- 2023GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognitioncited by 10
- 2025Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learningcited by 2
- 2025Manifold Approximation leads to Robust Kernel Alignmentcited by 2
- 2020Predicting Emotions Perceived from Soundscited by 1
- 2025Feature Selection via Graph Topology Inference for Soundscape Emotion Recognitioncited by 1
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