Aplicación de técnicas de clusterización para la clasificación de música dance electrónica

Abstract

Audio processing is one of the essential tasks for a data scientist, and audio analysis has applications in a diverse range of fields, such as medicine, telecommunications, improving sound quality in music production, and even military applications (filtering suspicious or terrorist audio). This project aims to use hard clustering techniques (such as k-means or k-nearest neighbor) and soft clustering techniques (such as fuzzy clustering) to classify input songs using different metrics. The classification methods will be used to segment previously processed input audios and obtain a sample of representative segments of the songs, determining their similarity with other songs of the same genre. Another technique that has proven effective for audio classification is convolutional neural networks (CNNs), which have been used in a wide range of fields. In the music field, they have been used to classify violin bowing techniques [1] and even detect potential heart problems using heartbeat sounds [2]. In this project, we will use this technique up to the point of feature extraction, and then use classical classification techniques to determine which group a section of a song belongs to.