Technological University Dublin ARROW@TU Dublin Doctoral Engineering 2010-01-01 Playing Technique and Violin Timbre: Detecting Bad Playing Jane Charles Technological University Dublin Follow this and additional works at: https://arrow.tudublin.ie/engdoc Recommended Citation Charles, J. (2010) Playing Technique and Violin Timbre: Detecting Bad Playing. Doctoral Thesis. Technological University Dublin. doi:10.21427/D7HC8P This Theses, Ph.D is brought to you for free and open access by the Engineering at ARROW@TU Dublin. It has been accepted for inclusion in Doctoral by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Playing Technique and Violin Timbre: Detecting Bad Playing Jane Ada Charles A thesis presented to the Dublin Institute of Technology, Faculty of Engineering, For the degree of Doctor of Philosophy 2010 Research Supervisors: Dr. Derry Fitzgerald Prof. Eugene Coyle II I certify that this thesis which I now submit for examination for the award of Doctor of Philosophy, is entirely my own work and has not been taken from the work of others, save and to the extent that such work has been cited and acknowledged within the text of my work. This thesis was prepared according to the regulations for postgraduate study by research of the Dublin Institute of Technology and has not been submitted in whole or in part for another award in any Institute or University. The work reported on in this thesis conforms to the principles and requirements of the Institute's guidelines for ethics in research. The Institute has permission to keep, lend or copy this thesis in whole or in part, on condition that any such use of the material of the thesis be duly acknowledged. Signature __________________________________ Date _________________________ III Abstract For centuries, luthiers have committed to working towards better understanding and improving the sound characteristics and playability of violins. With advances in technology and signal processing, studies attempting to define a violin’s sound quality via physical characteristics and resonance patterns have ensued. Existing work has primarily focused on physical aspects reflecting an instrument’s sound quality. In the music information retrieval domain, advances have been made in areas such as instrument identification tasks. Although much research has been completed on finding suitable features from which musical instruments can be represented, little work has focused on the violin’s complete timbre space and the effect a player has on the sound produced. This thesis specifically focuses on representing violin timbre such that a computer can detect the sound associated with a beginner from that of a professional standard player and detect typical beginner playing faults based on analysis of the waveform signal only. Work has been limited to nine playing faults considered by professional musicians to be typical of beginner violinists. In order to achieve these goals, it was necessary to create a suitable dataset consisting of an equal number of beginner and professional standard legato note samples. Feature extraction was then carried out by taking features from the time, spectral and cepstral domains. Selected features were then used to represent the samples in a classifier based on their efficacy at reflecting change within the violin’s timbre space. The dataset underwent the scrutiny of professional standard stringed instrument players via listening tests from which the target audience’s perception was captured. This information was verified and normalised before use as a priori labels in the classifier. Based on different feature representations, classification of violin notes reflecting perceived sound quality is presented in this thesis. The results show that it is possible to get a computer to determine between beginner and professional standard player legato notes and to detect playing faults. This thesis involves a thorough understanding of violin playing, its perception, suitable analysis methods, feature extraction, representation and classification. IV Acknowledgements The completion of this thesis would not have been possible without the help and advice of many people. I wish to thank my supervisors Dr. Derry Fitzgerald and Prof. Eugene Coyle for their help. Undertaking this work would not have been possible without the support, humour and rapturous enthusiasm exuded by all the musicians who offered up their ears, training and skills for experimentation. Special thanks go to Lioba Petrie, Ailleen Kelleher, Grainne Hope, Sinead Hope and Karla Charles for helping me obtain a data set from which the research could take place. Also essential to this work was all the time, expertise, advice given to me by Owen Tighe, sound engineer. Without Owen’s assistance, a master sound sample disk would not exist. Thank you. At one point during this research project I wanted to set up and observe Helmholtz motion on a violin. This was possible thanks to Finbar O’Meara and Ted Burke who helped source and set up the necessary equipment so that this could be carried out. Last but not least my family for their continued support. V Abbreviations AC autocorrelation CK spectral centroid kurtosis CM spectral centroid mean CQT constant Q transform CQTH constant Q transform harmonic bin content CV spectral centroid variance dB decibel DCT discrete cosine transform DFT discrete Fourier transform DSP digital signal processing FFCV four fold cross validation FFT fast Fourier transform HMM hidden Markov model Hz Hertz KLT Karhunen – Loève transform k-NN k-nearest neighbour LOOCV leave one out cross validation MFCC0 Mel frequency cepstrum first coefficient MFCC0M Mel frequency cepstrum first coefficients mean MFCC0S Mel frequency cepstrum first coefficients skew MFCC1S Mel frequency cepstrum second coefficients skew MFCC5 Mel frequency cepstrum sixth coefficient MFCCs Mel frequency cepstrum coefficients MMO Music Minus One VI MMV moving mean variance MPO Music Plus One NMF non-negative matrix factorisation PSD power spectral density PSD190 power spectral density below 190Hz RCC0 real cepstrum first coefficient RCC1 real cepstrum second coefficient RCC3 real cepstrum fourth coefficient RCC5 real cepstrum sixth coefficient RCCK real cepstrum coefficients kurtosis RCCM real cepstrum coefficients mean RCC real cepstrum coefficients RCCS real cepstrum coefficients skew RCCV real cepstrum coefficients variance RWC Real World Computing music database SCM spectral contrast measure SCM190 spectral contrast measure below 190Hz SF spectral flux SFM spectral flatness measure SFMK spectral flatness measure kurtosis SFMM spectral flatness measure mean SFMS spectral flatness measure skew SFMV spectral flatness measure variance SOM self-organising map STFT short-time Fourier transform SVD singular vector decomposition TK time domain kurtosis VII TM time domain mean TS time domain skew TV time domain variance Playing Fault Abbreviations BADE playing fault poor finish to a note BADS playing fault poor start to a note BB playing fault bow bouncing CR playing fault crunching INT playing fault poor intonation NV playing fault nervousness SE playing fault sudden end to note SK playing fault skating XN playing fault extra note VIII TABLE OF CONTENTS ABSTRACT.................................................................................................................. III ACKNOWLEDGEMENTS......................................................................................... IV ABBREVIATIONS ........................................................................................................V LIST OF FIGURES .......................................................................................................X LIST OF TABLES ...................................................................................................... XV 1 INTRODUCTION ....................................................................................................1 1.1 A BRIEF INTRODUCTION TO THE VIOLIN AND VIOLIN SOUND...............................3 1.2 VIOLIN PLAYING TECHNIQUE ...............................................................................5 1.3 CURRENT RESEARCH ............................................................................................7 1.4 MUSICAL SIGNAL REPRESENTATIONS.................................................................10 1.5 THESIS OUTLINE .................................................................................................15 1.6 ORIGINAL CONTRIBUTIONS.................................................................................15 2 PERCEPTION AND ANALYSIS OF VIOLIN TIMBRE .................................17 2.1 HEARING SOUND AND A MUSICIAN’S TRAINING.................................................17 2.2 PITCH, TIMBRE AND THE VIOLINIST....................................................................20 2.3 VIOLIN SOUND AND HELMHOLTZ MOTION .........................................................22 2.3.1 Effects of Bowing Technique on Helmholtz Motion..................................24 2.4 SUMMARY...........................................................................................................26 3 THE DATASET AND LISTENING TESTS .......................................................27 3.1 DATASET REQUIREMENTS...................................................................................27