Beat tracking is a common research area within music information retrieval (MIR). Jazz is a musical genre that is commonly rich in rhythmical complexity, which makes beat tracking challenging. The aim of this study is to analyze how well beat tracking algorithms detect the beats of jazz instruments. In this study, drums and double-bass were recorded in an ensemble but on single tracks. Three modern beat tracking algorithms were examined: LibRosa Dynamic, Essentia Multi-Feature, and madmom RNN. The algorithms’ beat trackings were evaluated using common metrics: the F-measure, P-score and Cemgil accuracy. The results showed that bass tracks generally got consistent results from all algorithms. However, all algorithms struggled with octave errors (the detected number of beats is off by a factor of two) and off-beats. When music was played without restrictions to the beat rhythm, madmom RNN generally performed the best, which suggests that machine learning with RNN (recurrent neural networks) is a good approach for beat tracking on rhythmically complex tracks. / Beat tracking är ett vanligt område inom music information retrieval (MIR). Jazz är en musikgenre som ofta är rytmisk komplex, vilket kan göra beat tracking utmanande. Denna studies syfte är att analysera hur väl beat tracking-algoritmer detekterar taktslagen hos jazzinstrument. I denna studie spelades trummor och kontrabas in samtidigt, men på separata spår. Tre moderna algoritmer för beat tracking undersöktes: LibRosa Dynamic, Essentia Multi-Feature, samt madmom RNN. Algoritmernas taktslagsestimeringar utvärderades utifrån vanligen tillämpade mätetal: F-measure, P-score och Cemgil accuracy. Resultaten visade att basspåren generellt sett gav konsekventa resultat från alla algoritmer. Däremot fick alla algoritmer oktavfel (antalet detekterade beats är fel med en faktor två) och off-beats. När musiken framfördes utan restriktioner i spelade taktslag presterade madmom RNN generellt bäst. Detta pekar mot att maskininlärning med RNN (recurrent neural networks) är en bra metod för beat tracking av rytmiskt komplexa spår.

Music mood recognition by machine continues to attract attention from both academia and industry. This thesis explores the hypothesis that the music emotion problem is circular, and is a primary step in determining the efficacy of circular regression as a machine learning method for automatic music mood recognition. This hypothesis is tested through experiments conducted using instances of the two commonly accepted models of affect used in machine learning (categorical and two-dimensional), as well as on an original circular model proposed by the author. Polygonal approximations of circular regression are proposed as a practical way to investigate whether the circularity of the annotations can be exploited. An original dataset assembled and annotated for the models is also presented. Next, the architecture and implementation choices of all three models are given, with an emphasis on the new polygonal approximations of circular regression. Experiments with different polygons demonstrate consistent and in some cases significant improvements over the categorical model on a dataset containing ambiguous extracts (ones for which the human annotators did not fully agree upon). Through a comprehensive analysis of the results, errors and inconsistencies observed, evidence is provided that mood recognition can be improved if approached as a circular problem. Finally, a proposed multi-tagging strategy based on the circular predictions is put forward as a pragmatic method to automatically annotate music based on the circular model. / Graduate / 0984 / 0800 / 0413 / zazz101@hotmail.com

Musical genres enable listeners to differentiate between diverse styles and forms of music, serving as a practical tool to organize and categorize artists, albums, and songs. Album covers, featuring graphic depictions that reflect the vibe and tone of the music, serve as a visual intermediary between the artist and the audience. While numerous machine learning techniques leverage textual, visual, and audio information in a multi-modal approach to categorize music, the sole focus on visual aspects, specifically album cover images, and their correlation with musical genres has been less explored. The question guides this research: How do EfficientNet and ResNet compare in their ability to accurately classify album cover images into specific genres based solely on visual features? Two state-of-the-art convolutional neural networks, ResNet and EfficientNet, are employed to classify a newly created dataset (the EquiGen dataset) of 60,000 album cover images into 15 distinct genres. The dataset was divided into 70% for training, 15% for validation, and 15% for testing.The findings reveal that both ResNet and EfficientNet achieve better-than-random classification accuracy, indicating that visual features alone can be informative for genre classification. Some genres performed much better than others, namely Metal, New Age and Rap. EfficientNet demonstrated slightly superior performance compared to ResNet, with higher accuracy, precision, recall, and F1 scores. However, both models exhibited challenges in generalizing well-to-unseen data and showed signs of overfitting.This study contributes to the interdisciplinary research on Music Genre Categorization (MGC), machine learning, and music.