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http://hdl.handle.net/2445/130799
Title: | Supervised learning for genre classification of audio tracks |
Author: | Bergantiños Yeste, Ángel |
Director/Tutor: | Escalera Guerrero, Sergio |
Keywords: | Sistemes classificadors (Intel·ligència artificial) Aprenentatge automàtic Programari Treballs de fi de grau Processament del so per ordinador Estils musicals Learning classifier systems Machine learning Computer software Computer sound processing Musical styles Bachelor's theses |
Issue Date: | 27-Jun-2018 |
Abstract: | [en] Music is a form of art that accompanies all of us every day and, with the appearance of on line services such as Spotify or Tidal, music analysis has become crucial to these services to recommend new music to users and to classify all new tracks uploaded every day. In this dissertation, we provide with a system for multi-class classification of genres for audio tracks. We base on standard MFCC audio descriptors to then define a compact audio track feature vector representation. Different machine learning classifiers are tested to perform final genre classification, also providing an analysis of the relevance of the different features. |
Note: | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Sergio Escalera Guerrero |
URI: | http://hdl.handle.net/2445/130799 |
Appears in Collections: | Programari - Treballs de l'alumnat Treballs Finals de Grau (TFG) - Enginyeria Informàtica |
Files in This Item:
File | Description | Size | Format | |
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codi_font.zip | Codi font | 44.97 kB | zip | View/Open |
memoria.pdf | Memòria | 1.49 MB | Adobe PDF | View/Open |
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