Training-Based Semantic Descriptors Modeling for Violin Quality Sound Characterization

Violin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. In this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. The most relevant semantic descriptors are collected through interviews to violin makers. These descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the Museo del Violino and of the International School of Luthiery both in Cremona. As sound description can vary throughout a performance, our approach also enables the modelling of time-varying (evolutive) semantic annotations.

[1]  Andrew J. Hunt,et al.  Timbral description of musical instruments , 2006 .

[2]  Petri Toiviainen,et al.  MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio , 2007, ISMIR.

[3]  A. Sarti,et al.  Bright Bow : Similarity Dark Bow : Opposition TimbreBow : Belong Definition Bow : Belong , 2014 .

[4]  Carleen M. Hutchins,et al.  A history of violin research , 1983 .

[5]  Seungmin Rho,et al.  SVR-based music mood classification and context-based music recommendation , 2009, ACM Multimedia.

[6]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Ewa Lukasik,et al.  Long Term Cepstral Coefficients for Violin Identification , 2010 .

[8]  J. Stepánek.  MUSICAL SOUND TIMBRE: VERBAL DESCRIPTION AND DIMENSIONS , 2006 .

[9]  D.P. Solomatine,et al.  AdaBoost.RT: a boosting algorithm for regression problems , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[10]  Yi-Hsuan Yang,et al.  A Regression Approach to Music Emotion Recognition , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[11]  W. Sethares.  Tuning, Timbre, Spectrum, Scale , 1998 .

[12]  Brian C J Moore,et al.  Exploring violin sound quality: investigating English timbre descriptors and correlating resynthesized acoustical modifications with perceptual properties. , 2012, The Journal of the Acoustical Society of America.

[13]  Augusto Sarti,et al.  Searching for dominant high-level features for Music Information Retrieval , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[14]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[15]  Derry Fitzgerald,et al.  Violin Timbre Space Features , 2006 .

[16]  Jan Stepanek,et al.  Evaluation of Timbre of Violin Tones According to Selected Verbal Attributes , 2002 .

[17]  Bruno L Giordano,et al.  Perceptual evaluation of violins: a quantitative analysis of preference judgments by experienced players. , 2012, The Journal of the Acoustical Society of America.

[18]  Toniann Pitassi,et al.  A Gradient-Based Boosting Algorithm for Regression Problems , 2000, NIPS.

[19]  Jim Woodhouse,et al.  The acoustics of the violin: a review , 2014, Reports on progress in physics. Physical Society.

[20]  Ewa Lukasik,et al.  MPEG-7 Audio Spectrum Basis as a signature of violin sound , 2007, 2007 15th European Signal Processing Conference.

[21]  Thomas Sikora,et al.  MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval , 2005 .

[22]  Youngmoo E. Kim,et al.  Feature selection for content-based, time-varying musical emotion regression , 2010, MIR '10.

[23]  M. Casey,et al.  MPEG-7 sound-recognition tools , 2001, IEEE Trans. Circuits Syst. Video Technol..

[24]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[25]  Yosuke Kurihara,et al.  Measurement and evaluation of violin tone quality , 2011, SICE Annual Conference 2011.

[26]  Muni S. Srivastava,et al.  Regression Analysis: Theory, Methods, and Applications , 1991 .