Download Report IEEE DataPort : Memo2496: Expert-Annotated Dataset and Dual-View Adaptive Framework for Music Emotion Recognition - 2025
NPY, IPYNB by Qilin Li, C. L. Philip Chen, Tong Zhang
Information
Format: NPY, IPYNB Publisher: IEEE DataPort Publication Date of the Electronic Edition: 12/10/2025
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ISBN: 10.21227/3824-wy49
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Description
Music emotion recognition delineates and categorises the spectrum of emotions expressed within musical compositions by conducting a comprehensive analysis of fundamental attributes, including melody, rhythm, and timbre. This task is pivotal for the tailoring of music recommendations, the enhancement of music production, the facilitation of psychotherapeutic interventions, and the execution of market analyses, among other applications. The cornerstone is the establishment of a music emotion recognition dataset annotated with reliable emotional labels, furnishing machine learning algorithms with essential training and validation tools, thereby underpinning the precision and dependability of emotion detection. The Music Emotion Dataset with 2496 Songs (Memo2496) dataset, comprising 2496 instrumental musical pieces annotated with valence-arousal (VA) labels and acoustic features, is introduced to advance music emotion recognition and affective computing. The dataset is meticulously annotated by 30 music experts proficient in music theory and devoid of cognitive impairments, ensuring an unbiased perspective. The annotation methodology and experimental paradigm are grounded in previously validated studies, guaranteeing the integrity and high calibre of the data annotations.
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Delivery time: Maximum 24 hours