The Recognition of Consonance Is not Impaired by Intonation Deviations: A Revised Theory

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The recognition of musical intervals is investigated comparing neurobiological and theoretical models (Tramo et al., The Biological Foundations of Music, Annals of the New York Academy of Sciences, 930, pp. 92–116, 2001; Ebeling, Verschmelzung und neuronale Autokorrelation als Grundlage einer Konsonanztheorie, Lang, Peter, Frankfurt/Main, 2007). The actual analyses focus on pitch tolerances of consonance identification. The mechanisms are different in models and the neurobiological process (pulse width and time latency) that the listener tolerates the deviation of the exact ratio of frequencies in the recognition of consonance. The neurobiological process is characterized by the spontaneous neural activity which is described by a Poisson distribution. Event related activities may be displayed in interspike-interval (ISI) and peri-event-time-histograms (PETH). Consonant musical intervals are characterized by periodicity in all-order ISI-histograms. This result is explained by the frequency ratio of the interval of pitches. These ISI-histograms also display subharmonics which are explainable as artifacts because of methodical issues. In contrast, the peridocity indicates the frequency of the residue. In order to adapt the model to reality, the width of the statistical distribution of the neural impulses should be considered. The spike-analysis for the recognition of periodicity is investigated on the basis of the statistical distribution and compared with the statistical results of the listener’s assessment of muscial intervals. The experimental data were taken from a study dealing with the assessment of intervals in a musical context (Fricke, Classification: The Ubiquitous Challenge, pp. 585–592, Berlin, Springer, 2005).

Keywords

Spike Train Basilar Membrane Integration Period Cochlear Duct Input Impulse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

I would like to thank Oliver Fricke for his statistical work, Christoph Reuter for his help preparing Figs. 1 and 2, and Michael Oehler for the English translation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Systematic Musicology, Institute for MusicologyUniversity of CologneCologneGermany

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