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This work augments the LALM with a dedicated frame-level grounding model while leveraging its semantic modeling capability to represent the event query, and shows that the grounding model can provide temporal evidence to support downstream reasoning.
The optimal spectrogram configuration for audio and speech analysis hinges on a nuanced interplay between front-end feature representation and back-end classifier architecture, varying significantly across tasks.
