2022
DOI: 10.1007/s10844-022-00746-0
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TROMPA-MER: an open dataset for personalized music emotion recognition

Abstract: We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and lan… Show more

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Cited by 33 publications
(8 citation statements)
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References 62 publications
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“…Multimodal datasets used in emotion-centric tasks, such as CAL500 (38), and AMC (39), combine audio features with emotion annotations. Additional datasets, including those from (40)(41)(42)(43)(44)(45), incorporate labels, lyrics, and participant information. Integrating lyrics with audio data provides additional context, enhancing emotion recognition accuracy.…”
Section: Emotion/affect Recognitionmentioning
confidence: 99%
“…Multimodal datasets used in emotion-centric tasks, such as CAL500 (38), and AMC (39), combine audio features with emotion annotations. Additional datasets, including those from (40)(41)(42)(43)(44)(45), incorporate labels, lyrics, and participant information. Integrating lyrics with audio data provides additional context, enhancing emotion recognition accuracy.…”
Section: Emotion/affect Recognitionmentioning
confidence: 99%
“…Information about the consistency in emotion ratings of music excerpts is not only important for purposes of emotion induction in experimental studies, but it also plays a critical role in work that uses signal processing and machine (deep) learning methods to characterize or predict musical emotions, such as music emotion recognition (MER; e.g., Gómez-Cañón et al, 2021;Gómez-Cañón et al, 2022;Han et al, 2022;Kim et al, 2010;Panda et al, 2023;Yang & Chen, 2011a). In work seeking to apply machine learning to music and emotion, emotion annotations of music by humans are essential for training machine learning algorithms for classification or recognition of music emotions.…”
Section: Interrater Agreement In Music-evoked Emotionmentioning
confidence: 99%
“…Characterizations of music-evoked emotion have also relied on concepts from basic emotions theory (Ekman, 1992a), notably happiness, sadness, fear, and anger, sometimes eclectically amplified by concepts that are not part of basic emotions but seem musically plausible to the researchers, such as tenderness or awe (Zentner & Eerola, 2010;Juslin et al, 2016). Examples for datasets with annotations inspired by basic emotions theory include Emotify (Aljanaki et al, 2016), MagnaTagATune (Law et al, 2009), Primary Musical Cues (Eerola, 2016;, SoundTracks (Eerola & Vuoskoski, 2011), and TROMPA [Towards Richer Online Music Public-domain Archives]-MER (Gómez-Cañón et al, 2022). The main limitation of basic emotions theory is that it was conceived to account for survival-type emotions such as anger, fear, or disgust, which Scherer and Zentner (2008) characterized as "utilitarian" as opposed to "aesthetic emotions".…”
Section: Describing Music-evoked Emotionmentioning
confidence: 99%
“…In the teaching process, more methods such as practicing vocals, studying music scores, and engaging in gatherings are used to improve students' basic level. At the same time, attention is also paid to academic research on choral singing and the study of choral history, cultural background, and musical personality [9]. Chinese universities focus on basic practice and learning in their teaching content, which is also a weakness compared to universities in other countries.…”
Section: Comparative Analysis Of Choir Teaching Models In Chinese And...mentioning
confidence: 99%