Please use this identifier to cite or link to this item:
Title: Using Support Vector Machines for Automatic Mood Tracking in Audio Music
Authors: Panda, Renato 
Paiva, Rui Pedro 
Keywords: Mood tracking; Music emotion recognition; Regression; Thayer
Issue Date: 13-May-2011
Publisher: Audio Engineering Society
Project: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music 
Serial title, monograph or event: 130th Audio Engineering Society Convention 2011 (AES 130)
Place of publication or event: London, UK
Abstract: In this paper we propose a solution for automatic mood tracking in audio music, based on supervised learning and classification. To this end, various music clips with a duration of 25 seconds, previously annotated with arousal and valence (AV) values, were used to train several models. These models were used to predict quadrants of the Thayer’s taxonomy and AV values, of small segments from full songs, revealing the mood changes over time. The system accuracy was measured by calculating the matching ratio between predicted results and full song annotations performed by volunteers. Different combinations of audio features, frameworks and other parameters were tested, resulting in an accuracy of 56.3% and showing there is still much room for improvement.
ISBN: 9781617829253
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

Show full item record

Page view(s)

checked on May 21, 2024


checked on May 21, 2024

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons