Papers by Arvind Krishna Sridhar
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models (2025.naacl-industry)
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| Challenge: | Recent literature focuses on constructing large audio language models (LALMs) but they are limited in temporal reasoning, which may hinder commercial applications . |
| Approach: | They propose a data augmentation technique for generating reliable audio temporal questions and answers using an LLM. |
| Outcome: | The proposed model performs well on public audio benchmark datasets and is optimized for edge applications. |
Audio Query Handling System with Integrated Expert Models and Contextual Understanding (2025.emnlp-industry)
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| Challenge: | Existing chatbots are limited to specific audio tasks, but the domain of audio content related queries remains underexplored. |
| Approach: | They propose to use an intent classifier to route queries to audio-related experts using a diverse audio query dataset. |
| Outcome: | The proposed system outperforms state-of-the-art LLMs on custom audio tasks and MMAU sound set benchmarks. |