Mamba-2 audio captioning: design space exploration and analysis

We present an audio captioning model built on the Mamba-2 large language model backbone, which is a state-of-the-art (SOTA) state-space model (SSM). We systematically explore the design space: LLM sizes, LoRA ranks, and connector designs leveraging Mamba-2's linear-time complexity with respect to sequence length. Across benchmarks, our models achieve strong captioning performance compared with larger language models trained on the same dataset, despite using fewer parameters. For the first time, we conduct an in-depth analysis of how the number of LLM parameters, audio encoder fine-tuning strategies, audio feature diversity, and different feature reduction or expansion techniques affect performance.