# Audio emotion recognition for atypical hearing

arXiv: https://arxiv.org/abs/2609.31168 · HTML: https://mlsanity.com/paper/2609.31168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Zero-Shot Audio Classification via Semantic Embeddings](https://mlsanity.com/paper/2011.12133/) (2020)
- [GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition](https://mlsanity.com/paper/2306.07848/) (2023)
- [Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning](https://mlsanity.com/paper/2505.16220/) (2025)
- [Manifold Approximation leads to Robust Kernel Alignment](https://mlsanity.com/paper/2510.22953/) (2025)
- [Predicting Emotions Perceived from Sounds](https://mlsanity.com/paper/2012.02643/) (2020)
- [Feature Selection via Graph Topology Inference for Soundscape Emotion Recognition](https://mlsanity.com/paper/2509.16760/) (2025)
- [Data-Driven Decoding of Russell's Circumplex Model of Affect](https://mlsanity.com/paper/2606.16843/) (2026)

## 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.