music-10

Music Emotion Recognition

3260 papers • 126 benchmarks • 313 datasets

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RAVDESS

VGMIDI

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Most implemented papers

Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities

G. Widmer, Shreyan Chowdhury, Verena Praher•Sun Jun 13 2021

This work merges audioLIME -- a source-separation based explainer -- with mid-level perceptual features, thus forming an intuitive connection chain between the input audio and the output emotion predictions, and demonstrates the usefulness of this method by applying it to debug a biased emotion prediction model.

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Music Mood Detection Based on Audio and Lyrics with Deep Neural Net

Romain Hennequin, Manuel Moussallam, Rémi Delbouys, Francesco Piccoli, Jimena Royo-Letelier•Tue Sep 18 2018

This work reproduces the implementation of traditional feature engineering based approaches and proposes a new model based on deep learning that outperforms classical models on the arousal detection task, and shows that both approaches perform equally on the valence prediction task.

97 0

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Semi-Supervised Music Emotion Recognition using Noisy Student Training and Harmonic Pitch Class Profiles

Hao Hao Tan•Tue Nov 30 2021

It is found that ensembling representations trained with different training lengths can improve tagging results significantly, which suggest a possible direction to explore incorporating multiple temporal resolutions in the network architecture for future work.

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Transformer-based approach towards music emotion recognition from lyrics

Yudhik Agrawal, R. Shanker, Vinoo Alluri•Tue Jan 05 2021

This study uses the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics to enhance web-crawlers' accuracy for extracting lyrics.

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Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics

Tibor L. R. Krols, Yana Nikolova, Ninell Oldenburg•Sat Feb 25 2023

It is found that out of the 11 high-level song features, mainly 5 contribute to the performance, multi-modal features do better than audio alone when predicting valence.

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