This work advances the music emotion recognition state-of-the-art by proposing novel emotionally-relevant audio features. 5We reviewed the existing audio features implemented in well-known frameworks and their relationships with the eight commonly 6 defined musical concepts. This knowledge helped uncover musical concepts lacking computational extractors, to which we propose 7 algorithms -namely related with musical texture and expressive techniques. To evaluate our work, we created a public dataset of 900 8 audio clips, with subjective annotations following Russell's emotion quadrants. The existent audio features (baseline) and the proposed 9 features (novel) were tested using 20 repetitions of 10-fold cross-validation. Adding the proposed features improved the F1-score to 10 76.4 percent (by 9 percent), when compared to a similar number of baseline-only features. Moreover, analysing the features relevance 11 and results uncovered interesting relations, namely the weight of specific features and musical concepts to each emotion quadrant, and 12 warrant promising new directions for future research in the field of music emotion recognition, interactive media, and novel music 13 interfaces.

The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work 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. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed.

This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams -baseline), adding other features, including novel features, improved the F-measure from 69.9%, 82.7% and 85.6% to 80.1%, 88.3% and 90%, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6% F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.

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