Abstract
This paper describes affective and psychophysiological foundations used to help to control affective content in music production. Our work includes the proposal of a knowledge base grounded on the state of the art done in areas of Music Psychology. This knowledge base has relations between affective states (happiness, sadness, etc.) and high level music features (rhythm, melody, etc.) to assist in the production of affective music. A computer system uses this knowledge base to select and transform chunks of music. The methodology underlying this system is essentially founded on Affective Computing topics. Psychophysiology measures will be used to detect listener’s affective state.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Scherer, K.: On the nature and function of emotion: A component process approach. Approaches to emotion, 293–317 (1984)
Scherer, K.: Psychological models of emotion. The Neuropsychology Of Emotion, 137–162 (2000)
Friberg, A.: A fuzzy analyzer of emotional expression in music performance and body motion. Music and Music Science (October 2004)
Taylor, R., Boulanger, P., Torres, D.: Visualizing emotion in musical performance using a virtual character. In: Butz, A., Fisher, B., Krüger, A., Olivier, P. (eds.) SG 2005. LNCS, vol. 3638, Springer, Heidelberg (2005)
Haag, A., Goronzy, S., Schaich, P., Williams, J.: Emotion recognition using bio-sensors: First steps towards an automatic system. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS (LNAI), vol. 3068, pp. 36–48. Springer, Heidelberg (2004)
Vyzas, E.: Recognition of Emotional and Cognitive States Using Physiological Data. PhD thesis, Massachusetts Institute Of Technology (1999)
Leman, M., Camurri, A.: Musical content processing for expressive gesture applications in interactive multimedia. In: Conference on Interdisciplinary Musicology (April 2004)
Leman, M., Vermeulen, V., De Voogdt, L., Taelman, J., Moelants, D., Lesaffre, M.: Correlation of gestural musical audio cues and perceived expressive qualities. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 40–54. Springer, Heidelberg (2004)
Castellano, G.: Experiments, analysis, and models of motor activation as a component of an emotional process. Master’s thesis, University of Genoa (2004)
Scherer, K., Zentner, M.: Emotional effects of music: Production rules. Music and emotion. Theory and research, 361–392 (2001)
Chiu, P., Kumar, A.: Music therapy: Loud noise or soothing notes? International Pediatrics 18(4), 204–208 (2003)
Hilliard, R.: Music therapy in hospice and palliative care: a review of the empirical data. Evidence-based Complementary and Alternative Medicine 2(2), 173–178 (2005)
Erkkilä, J., Lartillot, O., Luck, G., Riikkilä, K., Toiviainen, P.: Intelligent music systems in music therapy. Music Therapy Today 5 (2004)
Liljedahl, M., Sjömark, C., Lefford, N.: Using music to promote physical well-being via computer-mediated interaction. In: Musicnetwork Open Workshop, vol. 5, p. 5 (2005)
Wingstedt, J., Liljedahl, M., Lindberg, S., Berg, J.: Remupp: An interactive tool for investigating musical properties and relations. New Interfaces For Musical Expression, 232–235 (2005)
Fagerlönn, J.: A prototype using music responding to heart rate for stress reduction. Master’s thesis, Luleá University of Technology (June 2005)
Nakra, T.: Inside the Conductors Jacket: Analysis, Interpretation and Musical Synthesis of Expressive Gesture. PhD thesis, Massachusetts Institute of Technology (1999)
Nakra, T.M., Picard, R.: Analysis of affective musical expression with the conductors jacket. Col. Musical Informatics 12 (1998)
Healey, J., Picard, R., Dabek, F.: A new affect-perceiving interface and its application to personalized music selection. In: Workshop Perceptual User Interfaces (November 1998)
Casella, P., Paiva, A.: Magenta: An architecture for real time automatic composition of background music. In: de Antonio, A., Aylett, R., Ballin, D. (eds.) IVA 2001. LNCS (LNAI), vol. 2190, pp. 224–232. Springer, Heidelberg (2001)
Chung, J., Vercoe, G.: The affective remixer: Personalized music arranging. Conference on Human Factors in Computing Systems, 393–398 (2006)
Gabrielsson, A., Lindström, E.: The influence of musical structure on emotional expression. Music and emotion: Theory and research, 223–248 (2001)
Honing, H.: Computational modeling of music cognition: A case study on model selection. Music Perception 23(5), 365–376 (2006)
Widmer, G., Goebl, W., Intelligence, A., Vienna, A.: Computational models of expressive music performance: The state of the art. Journal of New Music Research 33(3), 203–216 (2004)
Schubert, E.: Measurement and Time Series Analysis of Emotion in Music. PhD thesis, University of New South Wales (1999)
Friberg, A.: pdm: An expressive sequencer with real-time control of the kth music-performance rules. Computer Music Journal 30(1), 37–48 (2006)
Schröder, M., Pirker, H., Lamolle, M.: First suggestions for an emotion annotation and representation language. International Conference On Language Resources And Evaluation, pp. 88–92 (May, 2006)
Lim, T., Loh, W., Shih, Y.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40(3), 203–228 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oliveira, A.P., Cardoso, A. (2007). Towards Affective-Psychophysiological Foundations for Music Production. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74889-2_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-74889-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74888-5
Online ISBN: 978-3-540-74889-2
eBook Packages: Computer ScienceComputer Science (R0)