Rozpoznávání žánru populárních skladeb

The aim of this thesis is to get acquainted with the principles of working with sound in the Python programming language and with the issue of convolutional neural networks in order to create a web application capable of recognizing the genre of an uploaded song. The thesis describes the principles of machine learning with a focus on convolutional neural networks. A considerable part of this thesis is devoted to the research of available datasets created for the purpose of music information retrieval. Next, the process of preparation of the selected dataset and transformation of audio information into spectrograms for the learning of convolutional neural networks is described. Two models capable of recognizing the genre of music were created as a part of the thesis. First, for general, more popular genres and the second focuses on subgenres of electronic music. The result is a web application that, after a song is uploaded, displays the probabilities of classification into individual genres.

Sound, music, music genres, popular songs, electronic music, Fourier transforms, spectrograms, machine learning, neural networks, convolutional neural networks.