Grant: Collaborative Research and Development Grants
Ryerson University
$97,333.00
A dynamic emotion feedback system for music using audio, symbolic, and physiological features
EF 2015-16
Résumé
This project builds on the accomplishments of a long-standing collaboration between WaveDNA, a music software development company in Toronto, and the SMART lab at Ryerson University. The project will focus on developing an emotion feedback system in a music composition and production workflow. Music composers, professional and amateur have specific emotional goals in mind during music creation. Additionally, they would be keen to know about the potential emotion appraisals made by listeners, while listening to their compositions. Despite the widespread use of digital audio workstations in the music production community, there currently exists no commercial software that provides emotion feedback to composers. The dynamic emotion feedback system proposed in this project, would not only benefit the composition process but would also strongly align WaveDNA with the trend in consumer products towards performance feedback through computational analytics.
From a research standpoint, although several studies have made advances in predicting emotion judgments of a ""general listener"" based on information from the music and audio characteristics, emotion prediction remains elusive because of its subjective nature. Preliminary work in the SMART lab has showed that physiological responses are powerful indicators of a listener's emotional state and may be used to model the listener's emotional responses to music. Given the lab's extensive experience in the area of music, emotion, and physiology, through this collaboration, we aim to (1) further current understanding of emotional response to music by developing and validating computational models using machine learning methods; (2) translate validated models into systems appropriate for practical use; and (3) integrate these emotion prediction models into WaveDNA's software suite.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
470378-2014
Name nom
Russo, Frank
Department département
Psychology
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2014
Award amount
97333
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
6307
Research subject group en
Psychology
Research subject group fr
Psychologie
Research subject en
Cognitive science -- other
Research subject fr
Science cognitive -- autres
Partie
1
Nb partie
3
Application title
A dynamic emotion feedback system for music using audio, symbolic, and physiological features
Keyword
NSERC
Application summary
This project builds on the accomplishments of a long-standing collaboration between WaveDNA, a music software development company in Toronto, and the SMART lab at Ryerson University. The project will focus on developing an emotion feedback system in a music composition and production workflow. Music composers, professional and amateur have specific emotional goals in mind during music creation. Additionally, they would be keen to know about the potential emotion appraisals made by listeners, while listening to their compositions. Despite the widespread use of digital audio workstations in the music production community, there currently exists no commercial software that provides emotion feedback to composers. The dynamic emotion feedback system proposed in this project, would not only benefit the composition process but would also strongly align WaveDNA with the trend in consumer products towards performance feedback through computational analytics.
From a research standpoint, although several studies have made advances in predicting emotion judgments of a ""general listener"" based on information from the music and audio characteristics, emotion prediction remains elusive because of its subjective nature. Preliminary work in the SMART lab has showed that physiological responses are powerful indicators of a listener's emotional state and may be used to model the listener's emotional responses to music. Given the lab's extensive experience in the area of music, emotion, and physiology, through this collaboration, we aim to (1) further current understanding of emotional response to music by developing and validating computational models using machine learning methods; (2) translate validated models into systems appropriate for practical use; and (3) integrate these emotion prediction models into WaveDNA's software suite.