Music Query by Emotions: Affect Sensing from Physiological Signals
Date Issued
2009
Date
2009
Author(s)
Chu, Wei-Rong
Abstract
The enormous volume of digital music collections is increasing the demand for querying music intelligently and efficiently. Given that music conveys and communicates emotion, emotion-based music query systems have emerged in recent years.owever, current media applications require users to input their emotion states manually. To achieve the goal of automatic music selection, this study presents a novel music query system via user’s physiological states. The system includes three parts. First, to recognize user’s physiological states, the system collects physiological signals and feeds them into the training module. Second, to estimate music emotion, the system extracts key features from audio track signals. Both the results of human and music emotion estimation are significant. Finally, the proposed system maps user’s physiological states to songs in the music database. In the third part, we present two query methods: single query and multiple queries. These are emotion mapping for a single state and a sequence of states respectively. This study establishes a novel music query system based on physiological signals. This framework can be extended for more sophisticated recommendation functions in the future.
Subjects
Affective Computing
Music Information Retrieval
Machine Learning
Type
thesis
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