Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/8048
Title: Reconeixement automàtic d'instruments musicals
Author: Castro Mayorgas, Juan Alberto
Director: Duxans Barrobés, Helenca
Others: Universitat Oberta de Catalunya
Keywords: Reconeixement automàtic d'instruments musicals;identificació d'instruments musicals;reconeixement de patrons;indexació automàtica;processament del senyal musical;reconeixement de timbre musical;automatic musical instruments recognition;musical instruments identification;pattern recognition;automatic indexing;musical signal processing;recognition of musical timbre;reconocimiento automático de instrumentos musicales;identificación de instrumentos musicales;reconocimiento de patrones;indexación automática;procesamiento de la señal musical;reconocimiento del timbre musical
Issue Date: Jun-2011
Publisher: Universitat Oberta de Catalunya
Abstract: The identification of musical instruments is one of the main areas of research to provide solutions to the need for automatic tools for indexing from the contents of recorded music. This paper studies the behavior of a set of relevant acoustic characteristics together with Gaussian Mixture Model, Support Vector Machines and k-Nearest Neighbor classification methods in the recognition of 18 musical instruments. Over 115,000 samples coming from TunedIt and another set of samples recorded by the author have been used for the experiments, reaching a success rate over 90 % in both cases. The study highlights that both the relevance of the acoustic parameters and the level of difficulty in identifying a particular instrument are related to the classifier.
Language: Catalan
URI: http://hdl.handle.net/10609/8048
Appears in Collections:Bachelor thesis, research projects, etc.

Share:
Export:
Files in This Item:
File Description SizeFormat 
jcastrom_TFC0611_memoria.pdfMemòria del projecte1.73 MBAdobe PDFView/Open
jcastrom_TFC0611_presentacio.ppsPresentació1.91 MBMicrosoft PowerpointView/Open
jcastrom_TFC0611_codi.zipCodi1.99 MBZipView/Open

This item is licensed under a Creative Commons License Creative Commons