MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
Towards unified music emotion recognition across dimensional and categori- cal models
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UNVERDICTED 4representative citing papers
KARMA-MV is a new benchmark showing that causal knowledge graphs improve VLMs on causal audio-visual reasoning in music videos.
Feedback-driven alignment with numerical rewards improves MusicLLM emotion regression on arousal and valence over instruction tuning alone while preserving MusicQA performance.
AMRS deploys a rollout-based causal transformer world model for offline DPO-based affective music recommendation under cold-start conditions on health platforms.
citing papers explorer
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MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
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KARMA-MV: A Benchmark for Causal Question Answering on Music Videos
KARMA-MV is a new benchmark showing that causal knowledge graphs improve VLMs on causal audio-visual reasoning in music videos.
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Aligning MusicLLM with Emotion using Instruction Tuning and Feedback-Driven Alignment
Feedback-driven alignment with numerical rewards improves MusicLLM emotion regression on arousal and valence over instruction tuning alone while preserving MusicQA performance.
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Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization
AMRS deploys a rollout-based causal transformer world model for offline DPO-based affective music recommendation under cold-start conditions on health platforms.