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LALMs can gain a far more precise sense of time by simply interleaving learned time embeddings into their audio feature sequences and then being fine-tuned with RL.
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.
