1 / 17

Quantitative Analysis of Phrasing Strategies in Expressive Performance:

Quantitative Analysis of Phrasing Strategies in Expressive Performance:. Computational Methods and Analysis of Performances of Unaccompanied Bach for Solo Violin. Authors : Eric Cheng and Elaine Chew Year: 2008 Presentation by : Elvira Burdiel Galende ELEM021, Queen Mary University

marnie
Download Presentation

Quantitative Analysis of Phrasing Strategies in Expressive Performance:

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. QuantitativeAnalysis of PhrasingStrategies in Expressive Performance: Computational Methods and Analysis of Performances of Unaccompanied Bach for Solo Violin Authors: Eric Cheng and Elaine Chew Year: 2008 Presentationby: Elvira BurdielGalende ELEM021, Queen Mary University 19 March 2012

  2. Introduction 1/2 • Goal – Quantitativeanalysis of phrasingstrategies of expressive performance in violin. • Andante movementfromBach’s Sonata No.2 in A minor BWV 1003 for solo violin • Regular pulse • Unambiguousphrasestructure • 11 recorded performances + average • Unlimitedmusicianfreedomonexpressive performance?

  3. Introduction 2/2 • Look forindicator of phrasingstrategy: • Tempo extraction • Loudness/dynamicsextraction • Analysis of phrasingstrategiesvia LMPD method • Phrasestrength - clarity • Phrasevolatility – variability of strength • Phrasetypicality – uniqueness of strategy

  4. Tempo extraction • Violin:softindeterminedonsets • No reliable and accurateautomatic beat tracking tool • Manual onsetdetectionusing a digital waveform editor. • Tempo = inverse of inter-onsetinterval • Rectangular smoothingwindow

  5. Dynamics extraction • Use twoexistingmodels: • 1- Single-band Leq (RLB) model • 2- Multiband PEAQ loudnessmodel • Sampleloudness at onsets • SmoothbyGaussianwindow • Test modelswith manual annotation • Correlationhigheron 2 -> methodchosen

  6. Extracted data • Global means and ranges • Sectionmeans and ranges • 4 sections: A A’ B B’ • Phrasemeans and ranges

  7. Extracted data - global

  8. Extract data – Sectionmeans

  9. Extract data – Sectionranges

  10. Extract data – Phrasemeans

  11. Extract data – Phrase

  12. Data extraction - Phrasing • Loudness -> more consistent

  13. LMPD Method • Local maximumonloudness per phrase • Phrasestrength (clarity) • Differencebetweenmax and adjacentmins • Phrasevolatility • Standard deviation of strengthover performance • Phrasetypicality (uniqueness) • Popularity of location of max

  14. LMPD results 1/2

  15. LMPD results 2/2

  16. LMPD Conclusions • Finite number of local maxima counts • Phrase strength and volatility relates to listening perception • Typicality (uniqueness) vs greatness of performance

  17. General conclusions • Significant differences between tempo and dynamics consistency with phrasing strategy • Different constraints of tempo and dynamics, different perceptions • Dynamics more consistent in this analysis, opposite on other studies • Cultural constructs

More Related