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New dataset and framework advance music emotion recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Memo2496, a new dataset for music emotion recognition featuring 2,496 instrumental tracks annotated by 30 music specialists with continuous valence-arousal labels. Alongside this dataset, they developed the Dual-view Adaptive Music Emotion Recogniser (DAMER) framework, which incorporates Dual-Stream Attention Fusion (DSAF), Progressive Confidence Labelling (PCL), and Style-Anchored Memory Learning (SAML). Evaluations on Memo2496 and other datasets demonstrated DAMER's superior performance in arousal accuracy and competitive valence accuracy. AI

IMPACT This research could lead to more accurate AI systems for understanding and generating music with specific emotional content.

RANK_REASON This is a research paper detailing a new dataset and framework for music emotion recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and framework advance music emotion recognition

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This is a research paper detailing a new dataset and framework for music emotion recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qilin Li, C. L. Philip Chen, Tong Zhang ·

    Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition

    arXiv:2512.13998v3 Announce Type: replace-cross Abstract: Music Emotion Recognition (MER) is constrained by limited expert annotations and the need to establish robustness across heterogeneous corpora. Memo2496 supplies a reproducible dataset of 2,496 instrumental tracks with con…

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