Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/94071
Title: Novel Audio Features for Music Emotion Recognition
Authors: Panda, Renato 
Malheiro, Ricardo 
Paiva, Rui Pedro 
Keywords: Affective computing; Audio databases; Emotion recognition; Feature extraction; Music information retrieval
Issue Date: 2020
Publisher: IEEE
Project: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music 
info:eu-repo/grantAgreement/FCT/SFRH/SFRH/BD/91523/2012/PT/EMOTION-BASED ANALYSIS AND CLASSIFICATION OF AUDIO MUSIC 
Volume: 11
Issue: 4
Abstract: This work advances the music emotion recognition state-of-the-art by proposing novel emotionally-relevant audio features. We reviewed the existing audio features implemented in well-known frameworks and their relationships with the eight commonly defined musical concepts. This knowledge helped uncover musical concepts lacking computational extractors, to which we propose algorithms - namely related with musical texture and expressive techniques. To evaluate our work, we created a public dataset of 900 audio clips, with subjective annotations following Russell's emotion quadrants. The existent audio features (baseline) and the proposed features (novel) were tested using 20 repetitions of 10-fold cross-validation. Adding the proposed features improved the F1-score to 76.4 percent (by 9 percent), when compared to a similar number of baseline-only features. Moreover, analysing the features relevance and results uncovered interesting relations, namely the weight of specific features and musical concepts to each emotion quadrant, and warrant promising new directions for future research in the field of music emotion recognition, interactive media, and novel music interfaces.
URI: http://hdl.handle.net/10316/94071
ISSN: 1949-3045
2371-9850
DOI: 10.1109/TAFFC.2018.2820691
Rights: embargoedAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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