Caracterização de sons confortáveis e stressantes através da aprendizagem máquina

Student thesis: Master's Thesis

Abstract

In this research, we have looked into a dichotomous characterization of sounds as either “Comfortable” or “Stressful”, through the use of machine learning. In addition to bringing light to information about underlying features the simple sounds that create a subjective medium for the listener, we envision that the results of this type of classification can contribute to the creation of an advisory system for the creation of sound design in user interfaces for products or applications. For the development of the system it was necessary to create a dataset. Lowlevel audio descriptors were then extracted for each instance of the dataset. Finally, we have used this data to feed machine learning algorithms. The results were evaluated in light of the common strategies in Music Information Retrieval (MIR) and indicated the viability of setting up an automatic sound characterization system.
Date of Award5 Jul 2016
Original languagePortuguese
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorPedro Duarte Pestana (Supervisor)

Keywords

  • Music information retrieval
  • Datasets
  • Machine learning
  • Sound categorization
  • Valencia
  • Affective computing
  • Music information
  • Retrieval

Designation

  • Mestrado em Som e Imagem

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