Identificación de versiones musicales (covers) utilizando aprendizaje maquinal

The task of recognizing when a song is a version or cover of another is a relatively easy task to do for humans when the song is known. However, to cause that a machine perform this work is complex due to the number of variables involved in preparing the cover, including variations in rhythm, tempo, instrumentation, genre and duration compared to the original version. In this project a methodology to identify covers from the application and analysis of machine-learning techniques, statistical signal processing and second order statistics was developed, in order to get that configuration to give the best results. For this we worked with the database Dataset Million Songs that gave us the metadata of the songs, from which data belonging to the acoustic characteristics of the song, such as pitches and timbres were used. Throughout the project we experimented with different data treatment techniques applied to the metadata provided by the database and we could see its usefulness to the task at hand. According to the results, a system that integrates processing frequency on pitches aligned with the beat, the implementation of a sparse coding and a data clustering system that showed a 63% correct identification of covers was obtained. Information on the possible use of supervised learning techniques combined with different types of metrics giving rise to future experiments to improve the results was also obtained.La tarea de reconocer cuándo una canción es una versión o cover de otra es una tarea relativamente fácil para el ser humano cuando se conoce la canción. Sin embargo, hacer que una máquina realice este trabajo resulta complejo debido al número de variables que se involucran en la elaboración del cover, mismas que incluyen variaciones en el ritmo, tempo, instrumentación, género y duración con respecto a la versión original. En este proyecto se desarrolló una metodología para identificar covers a partir de la aplicación y análisis de técnicas de aprendizaje maquinal, procesamiento de señales y estadística de segundo orden con la finalidad de obtener aquella configuración que diera los mejores resultados. Para esto se trabajó con la base de datos Million Songs Dataset que nos otorgó los metadatos de las canciones, a partir de los cuales se utilizaron los datos pertenecientes a las características acústicas de la canción, tales como, pitches y timbres. A lo largo del proyecto se experimentó con diferentes técnicas de tratamiento de los metadatos que proporcionó la base de datos y se pudo apreciar su utilidad para la tarea a desarrollar. De acuerdo a los resultados obtenidos, se obtuvo un sistema que integra un procesamiento en frecuencia sobre los pitches alineados con el beat, la aplicación de una codificación rala y un sistema de agrupamiento de datos que arrojó un 63% de identificación correcta de covers. También se obtuvo información sobre el posible uso de técnicas combinadas de aprendizaje supervisado con diferentes tipos de métricas dando pie a futuras experimentaciones para mejorar los resultados

Analyzing and improving genre and style classification in music through experiments

Music classification is a core task in the field of Music Information Retrieval (MIR). Classification refers to recognizing patterns in data. Music classification assigns genre, style, mood and etc. to each piece of music, to facilitate managing music data. It is an interesting topic in MIR with potential applications. There has been a considerable deal of attention focused on variety issues of music classification, such as selection appropriate feature sets, feature selection techniques, classification algorithm, etc. In this thesis, a series of empirical experiments are conducted to investigate and evaluate the genre and style classification in music. To validate our investigations and evaluations, several methods are proposed to analyze and interpret the results. In addition, we also design and implement an effective classification approach that obtains higher classification accuracy