Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain

Subtitles for the Deaf and Hard of Hearing : immersion through creative language [the Stranger Things case]

L'objectiu d'aquest estudi és explorar les característiques lingüístiques dels Subtítols per a Persones Sordes i amb Discapacitat Auditiva (SPS), posant un especial èmfasi en la seva creativitat. Concretament, busquem determinar com la creativitat lingüística dels SPS que descriuen música i efectes de so pot afectar l'experiència de gaudi. Per aconseguir aquest propòsit, hem classificat els SPS dels efectes de so i la música del darrer episodi de la sèrie de Netflix Stranger Things, titulat "The Piggyback", segons la taxonomia proposada per Tsaousi (2015), tenint en compte la seva funció exegètica, narrativa, contextual i emotiva. Amb aquest anàlisi, pretenem identificar patrons que puguin establir una relació entre la descripció creativa i la immersió, tenint en compte els comentaris del públic a les xarxes socials i les expectatives de les noves audiències. En última instància, el nostre objectiu final és abordar els SPS des d'una nova perspectiva, explorant les seves possibilitats creatives a nivell lingüístic i demostrant com això es tradueix en un major gaudi i una experiència més immersiva.El objetivo de este estudio es explorar las características lingüísticas de los Subtítulos para Personas Sordas y con Discapacidad Auditiva (SPS), poniendo un especial enfoque en su creatividad. En concreto, buscamos determinar cómo la creatividad lingüística de los SPS que describen música y efectos de sonido puede afectar la experiencia de disfrute. Para lograr este propósito, hemos clasificado los SPS de los efectos de sonido y las músicas del último episodio de la serie de Netflix Stranger Things, titulado "The Piggyback", según la taxonomía propuesta por Tsaousi (2015), teniendo en cuenta su función exegética, narrativa, contextual y emotiva. Con este análisis, pretendemos identificar patrones que puedan establecer una relación entre la descripción creativa y la inmersión, teniendo en cuenta los comentarios del público en las redes sociales y las expectativas de las nuevas audiencias. En última instancia, nuestro objetivo final es abordar los SPS desde una nueva perspectiva, explorando sus posibilidades creativas a nivel lingüístico y demostrando cómo ello se traduce en un mayor disfrute y una experiencia más inmersiva.The objective of this study is to explore the linguistic characteristics of Subtitles for the Deaf and Hard of Hearing (SDH) with a focus on its creativity. Specifically, the study aims to ascertain how the use of linguistic creativity in the description of music and sound effects affects enjoyment. To accomplish this objective, the ensemble of all sound and music descriptors from the final episode of the Netflix show Stranger Things, "The Piggyback", were classified based on Tsaousi's taxonomy according to their exegetic, narrative, contextual, and emotive functions. This analysis aims to identify possible patterns that could establish a relationship between creative description and immersion while taking into account the feedback provided by the audience through social media and the expectations of the new public. The ultimate objective of this research is to approach SDH from a new angle by exploring the possibilities of linguistic creativity and demonstrating how they result in a more immersive and enjoyable experience