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English(EN) Towards Unified Music Emotion Recognition across Dimensional and Categorical Models

新框架统一了跨标签类型的音乐情感识别

研究人员开发了一个新颖的多任务学习框架,以统一不同类型情感标签的音乐情感识别(MER),包括分类标签(例如,快乐、悲伤)和维度标签(例如,效价-唤醒度)。该框架整合了诸如调式和和弦等音乐特征与MERT嵌入,并采用知识蒸馏将学习从单个数据集转移到泛化学生模型。在MTG-Jamendo、DEAM、PMEmo和EmoMusic等数据集上的实验表明,这种方法显著提高了性能,在MTG-Jamendo数据集上超越了最先进的模型。 AI

排序理由 该集群包含一篇学术论文,详细介绍了音乐情感识别的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架统一了跨标签类型的音乐情感识别

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该集群包含一篇学术论文,详细介绍了音乐情感识别的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jaeyong Kang, Dorien Herremans ·

    迈向跨越维度和分类模型统一的音乐情感识别

    arXiv:2502.03979v3 Announce Type: replace-cross Abstract: One of the most significant challenges in Music Emotion Recognition (MER) comes from the fact that emotion labels can be heterogeneous across datasets with regard to the emotion representation, including categorical (e.g.,…