Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114653
Title: | Audio Features for Music Emotion Recognition: A Survey | Authors: | Panda, Renato Malheiro, Ricardo Paiva, Rui Pedro |
Keywords: | Affective computing; music emotion recognition; audio feature design; music information retrieval | Issue Date: | 2023 | Publisher: | IEEE | Project: | MERGE project financed by Fundação para Ciência e a Tecnologia (FCT) | Serial title, monograph or event: | IEEE Transactions on Affective Computing | Volume: | 14 | Issue: | 1 | Abstract: | The design of meaningful audio features is a key need to advance the state-of-the-art in music emotion recognition (MER). This article presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Previous MER surveys offered broad reviews, covering topics such as emotion paradigms, approaches for the collection of ground-truth data, types of MER problems and overviewing different MER systems. On the contrary, our approach is to offer a deep and specific review on one key MER problem: the design of emotionally-relevant audio features. | URI: | https://hdl.handle.net/10316/114653 | ISSN: | 1949-3045 2371-9850 |
DOI: | 10.1109/TAFFC.2020.3032373 | Rights: | openAccess |
Appears in Collections: | FCTUC Eng.Informática - Artigos em Revistas Internacionais I&D CISUC - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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Audio_Features_for_Music_Emotion_Recognition_A_Survey.pdf | 3.94 MB | Adobe PDF | View/Open |
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