Author(s): Panda, Renato ; Malheiro, Ricardo ; Paiva, Rui Pedro
Date: 2020
Persistent ID: https://hdl.handle.net/10316/94071
Origin: Estudo Geral - Universidade de Coimbra
Project/scholarship: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT ; info:eu-repo/grantAgreement/FCT/SFRH/SFRH/PT;
Subject(s): Affective computing; Audio databases; Emotion recognition; Feature extraction; Music information retrieval
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.
This work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e a Tecnologia (FCT) and Programa Operacional Temático Factores de Competitividade (COMPETE) – Portugal, as well as the PhD Scholarship SFRH/BD/91523/ 2012, funded by the Fundação para Ciência e a Tecnologia (FCT), Programa Operacional Potencial Humano (POPH) and Fundo Social Europeu (FSE).
