Istanbul Technical University 击 Graduate School of Arts
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ISTANBUL TECHNICAL UNIVERSITY F GRADUATE SCHOOL OF ARTS AND SOCIAL SCIENCES AUDIO BASED CLASSIFICATION OVER MUSICAL PRODUCTION PERIOD: A STUDY ON MUSICS OF BARI¸SMANÇO AND HIS CONTEMPORARIES M.A. THESIS Metehan KÖKTÜRK Department of Music Music Programme SEPTEMBER 2019 ISTANBUL TECHNICAL UNIVERSITY F GRADUATE SCHOOL OF ARTS AND SOCIAL SCIENCES AUDIO BASED CLASSIFICATION OVER MUSICAL PRODUCTION PERIOD: A STUDY ON MUSICS OF BARI¸SMANÇO AND HIS CONTEMPORARIES M.A. THESIS Metehan KÖKTÜRK (409151104) Department of Music Music Programme Thesis Advisor: Prof. Dr. Can KARADOGAN˘ SEPTEMBER 2019 ISTANBUL˙ TEKNIK˙ ÜNIVERS˙ ITES˙ I˙ F SOSYAL BIL˙ IMLER˙ I˙ ENSTITÜSÜ˙ MÜZIK˙ PRODÜKSIYON˙ DÖNEMI˙ ÜZERINE˙ SES TABANLI SINIFLANDIRMA: BARI¸SMANÇO VE ÇAGDA¸SLARININMÜZ˘ IKLER˙ I˙ ÜZERINE˙ BIR˙ ÇALI¸SMA YÜKSEK LISANS˙ TEZI˙ Metehan KÖKTÜRK (409151104) Müzik Anabilim Dalı Müzik Programı Tez Danı¸smanı:Prof. Dr. Can KARADOGAN˘ EYLÜL 2019 vi To my family, To the memories of my brother, whom I lost at the begining of this master program, To the memories of my grandfather, whom I lost at the end of this master program, vii viii FOREWORD I always found interesting the effect of the time period over the produced music, in means of technological availabilities and limitations, which is generally expressed with terms such as ”the sound of seventies” or ”the sound of eighties” etc. I wanted to take Barı¸sManço as a reference point, who is considered as one of the most influential figures of Turkish rock music, and move further with his contemporaries who are as influential and valuable as him. Among their valueable musical works, I aim to study on an audio classification implementation for evaluating the music production history of Turkey via today’s informatical possibilities and modern scientific study areas such as Music Information Retrieval and Machine Learning. For their guidance during this study, firstly I would like to thank to my thesis consultant Assoc. Dr. Can Karadogan,˘ and the jury members Dr. Taylan Özdemir and Dr. Ali Boyacı. I especially would like to thank to my friends Aykut Çayır, Doruk Tunaoglu˘ and Sezer Kutluk for enlightening my path in the field of Machine Learning. I also would like to thank to my friends Arzu ¸Sahin,Özgür Kuddar, Esin Yardımlı Alves Pereira, Asuman Kaya and Ekrem Yigit˘ Türüdü for their support and motivation during the writing process. SEPTEMBER 2019 Metehan KÖKTÜRK ix x TABLE OF CONTENTS Page FOREWORD........................................................................................................... ix TABLE OF CONTENTS........................................................................................ xi ABBREVIATIONS ................................................................................................. xiii LIST OF TABLES .................................................................................................. xv LIST OF FIGURES ................................................................................................xvii SUMMARY ............................................................................................................. xix ÖZET ....................................................................................................................... xxi 1. INTRODUCTION .............................................................................................. 1 2. BARI¸SMANÇO AND HIS CONTEMPORARIES ........................................ 3 2.1 Barı¸sManço’s Music Career .......................................................................... 3 2.2 Bands that Barı¸sManço Worked with............................................................ 4 2.3 Record Companies that Barı¸sManço Worked with ....................................... 5 2.4 Barı¸sManço Discography .............................................................................. 5 2.5 Contemporaries of Barı¸sManço..................................................................... 5 3. AUDIO FEATURE EXTRACTION TOOLS................................................... 7 3.1 Overview for Modern Audio Feature Extraction Tools.................................. 7 3.2 Evaluation of Audio Feature Extraction Tools ............................................... 9 3.3 Selected Feature Extraction Tool for This Study............................................ 11 4. METHODOLOGY ............................................................................................. 13 4.1 Audio Collection ............................................................................................ 13 4.1.1 Music Pieces of Barı¸sManço................................................................. 13 4.1.2 Music Pieces of Barı¸sManço’s Contemporaries.................................... 13 4.2 Technologies Used.......................................................................................... 14 4.3 Feature Extraction from Audio....................................................................... 14 4.4 Dataset of Low-Level Features....................................................................... 18 4.4.1 Extracted Excerpts from Barı¸sManço’s Works ..................................... 19 4.4.2 Extracted Excerpts from Musics of Barı¸sManço’s Contemporaries ..... 19 4.5 Feature Selection ............................................................................................ 21 4.5.1 Removing Redundant Features............................................................... 21 4.5.2 Recursive Feature Elimination with Cross Validation............................ 23 4.5.3 Feature Importances with Forests of Trees............................................. 23 4.6 Machine Learning Algorithms ....................................................................... 23 4.6.1 Logistic Regression (LR) ....................................................................... 23 4.6.2 Support Vector Machines (SVM) ........................................................... 24 4.6.3 K-Nearest Neighbors (KNN).................................................................. 24 4.6.4 Gradient Tree Boosting (GTB)............................................................... 25 4.6.5 Random Forests (RF) ............................................................................. 25 xi 4.6.6 Multi-Layer Perceptron (MLP) .............................................................. 26 4.6.7 Ensemble Vote Classifier (EVC) ............................................................ 26 5. RESULTS AND EVALUATION........................................................................ 27 5.1 Feature Selection Results ............................................................................... 27 5.1.1 Removing Redundant Features............................................................... 27 5.1.2 Recursive Feature Elimination with Cross Validation............................ 27 5.1.3 Feature Importance with Forests of Trees .............................................. 28 5.2 Results of the Machine Learning Models....................................................... 29 5.2.1 Logistic Regression ................................................................................ 29 5.2.2 Support Vector Machines ....................................................................... 31 5.2.3 K-Nearest Neighbors.............................................................................. 31 5.2.4 Gradient Tree Boosting .......................................................................... 32 5.2.5 Random Forests ...................................................................................... 33 5.2.6 Multi-Layer Perceptron .......................................................................... 34 5.2.7 Ensemble Vote Classifier........................................................................ 35 5.3 Testing the Best Resulting Classifiers on Extended Dataset .......................... 36 5.3.1 Support Vector Machines on Extended Dataset ..................................... 37 5.3.2 Random Forests on Extended Dataset.................................................... 37 5.3.3 Ensemble Vote Classifier on Extended Dataset...................................... 37 6. CONCLUSION ................................................................................................... 39 6.1 Practical Application of This Study ............................................................... 40 6.2 Further Work................................................................................................... 41 REFERENCES........................................................................................................ 43 APPENDICES......................................................................................................... 47 CURRICULUM VITAE......................................................................................... 63 xii ABBREVIATIONS rpm : Revolutions Per Minute Hz : Hertz kHz : Kilohertz dB : Decibel SPL : Sound Pressure Level DAW : Digital Audio Workstation LR : Logistic Regression SVM : Support Vector Machines KNN : K-Nearest Neighbors GTB : Gradient Tree Boosting RF : Random Forest MLP : Multi-Layer Perceptron EVC : Ensemble Vote Classifier xiii xiv LIST OF TABLES Page Table 5.1 : Results of Recursive Feature Elimination Iterations........................... 28 Table 5.2 : Success Rates of Classifier Algorithms with Different Number of Features............................................................................................... 29 Table 5.3 : Comparison of Success Rates