ABSTRACT
This study presents a listening experiment designed to further examine the previously proposed luminance-texture-mass (LTM) model for timbral semantics. Thirty two musically trained listeners rated twenty four instrument tones on six predefined semantic scales, namely, brilliance, depth, roundness, warmth, fullness and richness. The selection of this limited set of descriptors was based on previous exploratory work. These six semantic scales were analysed through Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) to produce two different timbre spaces. These timbre spaces were subsequently compared for configurational and dimensional similarity with the LTM semantic space and the direct MDS perceptual space obtained with the same stimuli. The results showed that the selected six semantic scales are adequately representing the LTM model and are fair at predicting the configurations of the sounds that result from pairwise dissimilarity ratings.
- H. Abdi. The RV coefficient and the congruence coefficient. In N. Salkind, editor, Encyclopedia of Measurement and Statistics. SAGE Publications, Inc., 2455 Teller Road, Thousand Oaks, California, 91320, United States, 2007.Google Scholar
- V. Alluri and P. Toiviainen. Exploring perceptual and acoustical correlates of polyphonic timbre. Music Perception, 27(3):223--242, 2010.Google ScholarCross Ref
- A. Caclin, S. McAdams, B. K. Smith, and S. Winsberg. Acoustic correlates of timbre space dimensions: A confirmatory study using synthetic tones. Journal of the Acoustical Society of America, 118(1):471--482, July 2005.Google ScholarCross Ref
- P. D. Ellis. The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results, pages 31--44. Cambridge University Press, Cambridge: New York, 1st edition, 2010.Google Scholar
- J. C. Gower. A general coefficient of similarity and some of it's properties. Biometrics, 27(4):857--871, 1971.Google ScholarCross Ref
- J. C. Gower. Generalized procrustes analysis. Psychometrika, 40(1):33--51, 1975.Google ScholarCross Ref
- J. C. Gower and G. B. Dijksterhuis. Procrustes Problems, pages 1--248. Oxford University Press, Oxford; New York, 2004.Google Scholar
- J. M. Grey. Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61:1270--1277, 1977.Google ScholarCross Ref
- J. M. Hajda. Analysis, Synthesis, and Perception of Musical Sounds, chapter The Effect of Dynamic Acoustical Features on Musical Timbre, pages 250--271. Springer, New York, USA, 2007.Google Scholar
- P. Iverson and C. L. Krumhansl. Isolating the dynamic attributes of musical timbre. Journal of the Acoustical Society of America, 94(5):2595--2603, 1993.Google ScholarCross Ref
- J. Josse and S. Holmes. Measures of dependence between random vectors and tests of independence. Literature review. arXiv:1307.7383.{stat}, 2013.Google Scholar
- R. A. Kendall and E. C. Carterette. Verbal attributes of simultaneous wind instrument timbres: I. von Bismarck's adjectives. Music Percept., 10(4):445--468, 1993a.Google ScholarCross Ref
- R. A. Kendall and E. C. Carterette. Verbal attributes of simultaneous wind instrument timbres: II. Adjectives induced from Piston's Orchestration. Music Percept., 10(4):469--502, 1993b.Google ScholarCross Ref
- R. A. Kendall, E. C. Carterette, and J. M. Hajda. Perceptual and acoustical features of natural and synthetic orchestral instrument tones. Music Percept., 16:327--364, 1999.Google ScholarCross Ref
- T. Lokki, J. Pätynen, A. Kuusinen, H. Vertanen, and S. Tervo. Concert hall acoustics assessment with individually elicited attributes. Journal of the Acoustical Society of America, 130(2):835--849, 2011.Google ScholarCross Ref
- C. D. Mayer, J. Lorent, and G. W. Horgan. Exploratory analysis of multiple omics datasets using the adjusted RV coefficient. Statistical Applications in Genetics and Molecular Biology, 10(1):1--27, 2011.Google ScholarCross Ref
- J. R. Miller and E. C. Carterette. Perceptual space for musical structures. Journal of the Acoustical Society of America, 58(3):711--720, 1975.Google ScholarCross Ref
- J. Oksanen. Multivariate analysis of ecological communities in R: vegan tutorial, 2013. University Oulu, Finland.Google Scholar
- F. Opolko and J. Wapnick. McGill University Master Samples collection on DVD. McGill University, Montréal, Québec, Canada, 2006.Google Scholar
- P. R. Peres-Neto and D. A. Jackson. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia, 129(2):169--178, 2001.Google ScholarCross Ref
- R. Plomp. Frequency Analysis and Periodicity Detection in Hearing, chapter Timbre as a multidimensional attribute of complex tones, pages 397--414. Sijthoff, Leiden, 1970.Google Scholar
- R. Plomp. Aspects of Tone Sensation: A Psychophysical Study, chapter Timbre of complex tones, pages 85--110. Academic Press, London, 1976.Google Scholar
- A. K. Smilde, H. a. L. Kiers, S. Bijlsma, C. M. Rubingh, and M. J. van Erk. Matrix correlations for high-dimensional data: the modified RV-coefficient. Bioinformatics (Oxford, England), 25(3):401--405, 2009. Google ScholarDigital Library
- G. von Bismarck. Timbre of steady tones: A factorial investigation of its verbal attributes. Acustica, 30:146--159, 1974.Google Scholar
- H. L. F. von Helmholtz. On the Sensations of Tone as a Physiological Basis for the Theory of Music. New York: Dover (1954), 4 edition, 1877. English translation by A. J. Ellis.Google Scholar
- A. Zacharakis, K. Pastiadis, and J. D. Reiss. An interlanguage study of musical timbre semantic dimensions and their acoustic correlates. Music Perception, 31(4):339--358, 2014.Google ScholarCross Ref
- A. Zacharakis, K. Pastiadis, and J. D. Reiss. An interlanguage unification of musical timbre: bridging semantic, perceptual and acoustic dimensions. Music Perception, 32(4):394--412, 2015.Google ScholarCross Ref
Index Terms
- A confirmatory approach of the Luminance-Texture-Mass model for musical timbre semantics
Recommendations
An Effective Feature Calculation For Analysis & Classification of Indian Musical Instruments Using Timbre Measurement
ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015Musical instrument recognition is significant field in the research of computer music which is related to the modelling of sounds. Analysing & synthesing the structure of musical note is of importance both for modelling music signals and their automatic ...
Timbre Analysis and Synthesis of Stringed Musical Instruments
ICICIC '06: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1In this paper, we discuss and propose how to analyze and synthesize the timbre of stringed mzlsical instruments, especially electric bass guitars and pianos. The structure of harmonics' elements on the Fourier spectrum is investigated. And a novel ...
Musical composition style transfer via disentangled timbre representations
IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial IntelligenceMusic creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been ...
Comments