Modelling Emotional Effects of Music: Key Areas of Improvement

Modelling emotions perceived in music and induced by music has garnered increased attention during the last five years. The present paper attempts to put together observations of the areas that need attention in order to make progress in the modelling emotional effects of music. These broad areas are divided into theory, data and context, which are reviewed separately. Each area is given an overview in terms of the present state of the art and promising further avenues, and the main limitations are presented. In theory, there are discrepancies in the terminology and justifications for particular emotion models and focus. In data, reliable estimation of high-level musical concepts and data collection and evaluation routines require systematic attention. In context, which is the least developed area of modelling, the primary area of improvement is incorporating musical context (music genres) into the modelling emotions. In a broad sense, better acknowledgement of music consumption and everyday life context, such as the data provided by social media, may offer novel insights into the modelling emotional effects of music

Music emotion recognition: a multimodal machine learning approach

Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major selection preferred by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for innovative and adaptable search mechanisms, which can be personalized according to users’ emotional state, has gained increasing consideration in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semisupervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the crossvalidated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performanc

The role of artist and genre on music emotion recognition

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this study is to classify a dataset of songs according to their emotion and to understand the impact that the artist and genre have on the accuracy of the classification model. This will help market players such as Spotify and Apple Music to retrieve useful songs in the right context. This analysis was performed by extracting audio and non-audio features from the DEAM dataset and classifying them. The correlation between artist, song genre and other audio features was also analyzed. Furthermore, the classification performance of different machine learning algorithms was evaluated and compared, e.g., Support Vector Machines (SVM), Decision Trees, Naive Bayes and K-Nearest Neighbors. We found that Support Vector Machines attained the highest performance when using either only Audio features or a combination of Audio Features and Genre. Namely, an F-measure of 0.46 and 0.45 was achieved, respectively. We concluded that the Artist variable was not impactful to the emotion of the songs. Therefore, by using Support Vector Machines with the combination of Audio and Genre variables, we analyzed the results and created a dashboard to visualize the incorrectly classified songs. This information helped to understand if these variables are useful to improve the emotion classification model developed and what were the relationships between them and other audio and non-audio features