Abstract
Audio feature estimation is potentially improved by including the auditory short-term memory (STM) model. A new paradigm of audio feature estimation is obtained by adding the influence of notes in the STM. These notes are identified using the directional spectral flux, and the spectral content that is increased by the new note is added to the STM. The STM is exponentially fading with time span and number of elements, and each note only belongs to the STM for a limited time. Initial investigations regarding the behavior of the STM shows promising results, and an initial experiment with sensory dissonance has been undertaken with good results. The parameters obtained from the auditory memory model, along with the dissonance measure, are shown here to be of interest in music genre classification.
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
Atkinson, R.C., Shiffrin, R.M.: Human memory: A proposed system and its control processes. In: Spence, K.W., Spence, J.T. (eds.) The Psychology of Learning and Motivation, vol. 2, pp. 89–195. Academic Press, New York (1968)
Pashler, H., Carrier, M.: Structures, Processes, and the Flow of Information. In: Bjork, Bjork (eds.) Memory: Handbook of Perception and Cognition, pp. 3–29. Academic Press (1996)
Snyder, B.: Music and Memory. An Introduction. The MIT Press, Cambridge (2000)
Baddeley, A.D., Hitch, G.: Working memory. In: Bower, G.H. (ed.) The Psychology of Learning and Motivation: Advances in Research and Theory, vol. 8, pp. 47–89. Academic Press, New York (1974)
Baddeley, A.D.: The episodic buffer: a new component of working memory? Trends in Cognitive Science 4, 417–423 (2000)
Miller, G.A.: The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review 63(2), 81–97 (1956)
Gross, R.: Psychology: The Science of Mind and Behaviour. Hodder Arnold Publication (2005)
Massaro, D., Loftus, G.R.: Sensory and Perceptual Storage. In: Bjork, E.L., Bjork, R.A. (eds.) Memory, pp. 86–99. Academic Press, San Diego (1996)
Foote, J.: A similarity measure for automatic audio classification. In: Proceedings AAAI 1997 Spring Symposium on Intelligent Integration and Use of Text, Image, Video, and Audio Corpora, Stanford, Palo Alto, California, USA (1997)
McNab, R.J., Smith, L.A., Witten, I.H., Henderson, C.L., Cunningham, S.J.: Towards the digital music library: Tune retrieval from acoustic input. In: Proceedings DL 1996, pp. 11–18 (1996)
Rolland, P.Y., Raskinis, G., Ganascia, J.G.: Musical content-based retrieval: an overview of the Melodiscov approach and system. ACM Multimedia 1, 81–84 (1999)
Ghias, A., Logan, J., Chamberlin, D., Smith, B.C.: Query by humming - musical information retrieval in an audio database. In: Proceedings Multimedia, pp. 231–236 (2001)
Pauws, S., Eggen, B.: PATS: Realization and user evaluation of an automatic playlist generator. In: Proceedings of the 3rd ISMIR, Ircam, France, pp. 222–230 (2002)
Anderson, J.R., Lebiere, C.: Atomic components of thought, Hillsdale, NJ (1998)
Moore, B.C.J.: Psychology of Hearing. Academy Press (1997)
Jensen, K.: Multiple scale music segmentation using rhythm, timbre and harmony. EURASIP Journal on Applied Signal Processing, Special issue on Music Information Retrieval Based on Signal Processing (2007)
Plomp, R., Levelt, W.J.M.: Tonal Consonance and Critical Bandwidth. J. Acoust. Soc. Am. 38(4), 548–560 (1965)
Sethares, W.: Local consonance and the relationship between timbre and scale. J. Acoust. Soc. Am. 94(3), 1218–1228 (1993)
Meng, A.: Temporal feature integration for music organization. Ph.D. dissertation, IMM, Denmark Technical University (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, K. (2012). Music Genre Classification Using an Auditory Memory Model. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K., Mohanty, S. (eds) Speech, Sound and Music Processing: Embracing Research in India. CMMR FRSM 2011 2011. Lecture Notes in Computer Science, vol 7172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31980-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-31980-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31979-2
Online ISBN: 978-3-642-31980-8
eBook Packages: Computer ScienceComputer Science (R0)