Music Information Retrieval Techniques for Determining the Place of Origin of a Music Interpretation

Determining the place of origin of the musical compositions is a modern area of research in the field of music information retrieval (MIR). The musical interpretation of one piece carries a variety of author's intentions that influence the musical character of the resulting composition. These aspects may include rhythm, dynamics, timbre, or tonality. This paper introduces a novel methodology for determining the place of origin of a music interpretation based on advanced signal processing and machine learning techniques. For this purpose, we collected a database of 35 different interpretations of Leos Janacek's String Quartet No.1, “Kreutzer Sonat”: IV. Con Moto-Adagio. Employing random forests classifier, we achieved classification accuracy over 97 % using features derived from Mel-frequency cepstral coefficients. This paper proves it is possible to use MRI for determining the origin of a music interpretation with very high accuracy.

[1]  P. Laukka,et al.  A dimensional approach to vocal expression of emotion , 2005 .

[2]  François Pachet,et al.  A taxonomy of musical genres , 2000, RIAO.

[3]  David Salomon Handbook of Data Compression, 5/E. , 2018 .

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

[5]  Michael Edwards,et al.  Algorithmic composition , 2011, Commun. ACM.

[6]  José Fornari,et al.  Multi-Feature Modeling of Pulse Clarity: Design, Validation and Optimization , 2008, ISMIR.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Arshia Cont,et al.  A unified approach to real time audio-to-score and audio-to-audio alignment using sequential Montecarlo inference techniques , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[10]  Ryan Stables,et al.  Subjective comparison of music production practices using the Web Audio Evaluation Tool , 2016 .

[11]  Charles Dodge,et al.  Computer Music: Synthesis, Composition, and Performance , 1997 .

[12]  Meinard Mller,et al.  Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications , 2015 .

[13]  Goutam Saha,et al.  Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition , 2012, Speech Commun..

[14]  B. Matthews Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[15]  Eamonn J. Keogh,et al.  Iterative Deepening Dynamic Time Warping for Time Series , 2002, SDM.

[16]  Joseph Rothstein,et al.  MIDI: A Comprehensive Introduction , 1992 .

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Peter Knees,et al.  Music Similarity and Retrieval: An Introduction to Audio- and Web-based Strategies , 2016 .

[19]  Pedro Vera-Candeas,et al.  Tempo Driven Audio-to-Score Alignment Using Spectral Decomposition and Online Dynamic Time Warping , 2016, ACM Trans. Intell. Syst. Technol..

[20]  Patrick Susini,et al.  The Timbre Toolbox: extracting audio descriptors from musical signals. , 2011, The Journal of the Acoustical Society of America.

[21]  Joshua D. Reiss,et al.  An Evaluation of Audio Feature Extraction Toolboxes , 2015 .

[22]  Petri Toiviainen,et al.  A Matlab Toolbox for Music Information Retrieval , 2007, GfKl.

[23]  Stéphane Marchand-Maillet Adaptive Multimedia Retrieval: User, Context, and Feedback, 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers , 2007, Adaptive Multimedia Retrieval.

[24]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[25]  Marc Leman,et al.  Synchronizing multimodal recordings using audio-to-audio alignment , 2015, Journal on Multimodal User Interfaces.

[26]  Alexander Lerch,et al.  An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics , 2012 .

[27]  Meinard Müller,et al.  An Efficient Multiscale Approach to Audio Synchronization , 2006, ISMIR.

[28]  R. Kronland-Martinet,et al.  From Clarinet Control to Timbre Perception , 2010 .

[29]  Giovanni Motta,et al.  Handbook of Data Compression , 2009 .

[30]  Jeroen Breebaart,et al.  Features for audio and music classification , 2003, ISMIR.