Computational study demonstrates high-throughput audio question answering using GPU memory caching and bilinear gated fusion, highlighting scalable pathways for multimodal acoustic reasoning.
This paper presents a high-efficiency multimodal framework designed for Audio Question Answering (AQA). To overcome standard CPU and disk I/O bottlenecks, the architecture implements a zero-I/O data pipeline that pre-processes and caches 10,686 samples directly into the 48 GB VRAM of an NVIDIA RTX 6000 Ada Generation GPU during initialization. The model combines a 2D Residual Network (ResNet) audio encoder with an unfrozen 6-layer BERT-base text encoder using a Bilinear Gated Fusion mechanism. Trained across 15 epochs with EMA weight stabilization, the system achieves an extraction throughput of 128-131 files per second and epoch durations of 30-40 seconds, establishing a robust baseline for multimodal acoustic reasoning tasks.
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Jha et al. (2026) studied this question.
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