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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
Treballs de fi de grau
Música contemporània
Estils musicals
Learning classifier systems
Machine learning
Computer software
Contemporary music
Musical styles
Bachelor's thesis
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
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

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