This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. Design and development of an Indian classical vocal training tool Sharma, Shraddha 2019 Sharma, S. (2019). Design and development of an Indian classical vocal training tool. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/136780 https://doi.org/10.32657/10356/136780 This work is licensed under a Creative Commons Attribution‑NonCommercial 4.0 International License (CC BY‑NC 4.0). Downloaded on 26 Sep 2021 16:16:09 SGT DESIGN AND DEVELOPMENT OF AN INDIAN CLASSICAL VOCAL TRAINING TOOL SHRADDHA SHARMA SCHOOL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2019 DESIGN AND DEVELOPMENT OF AN INDIAN CLASSICAL VOCAL TRAINING TOOL SHRADDHA SHARMA School of Electrical and Electronics Engineering A thesis submitted to the Nanyang Technological University in partial fulfilment of the requirement for the degree of Master of Engineering 2019 Statement of Originality I hereby certify that the work embodied in this thesis is the result of original research, is free of plagiarised materials, and has not been submitted for a higher degree to any other University or Institution. 28/03/2019 . Date Shraddha Sharma Supervisor Declaration Statement I have reviewed the content and presentation style of this thesis and declare it is free of plagiarism and of sufficient grammatical clarity to be examined. To the best of my knowledge, the research and writing are those of the candidate except as acknowledged in the Author Attribution Statement. I confirm that the investigations were conducted in accord with the ethics policies and integrity standards of Nanyang Technological University and that the research data are presented honestly and without prejudice. 28/03/2019 . Date Prof Wen Changyun Authorship Attribution Statement This thesis contains material from one paper accepted at conference in which I am listed as an author. Chapter 2 and 3 is going to publish (in press) as Shraddha Sharma and Meng Joo Er, “Design and Development of an Indian Classical Vocal Training Tool”, 2nd International Conference on Intelligent Autonomous Systems, 2019. The contribution of the co-authors is as follows: • Prof Er Meng Joo provided the initial project direction and helped in editing the manuscript drafts. • I designed the tool and collected all the necessary data for the calibration of the tool. • The evaluation parameters were defined by me for the analysis of the audio signal. I produced the results and prepared the manuscript drafts. 28/03/2019 . Date Shraddha Sharma vi Acknowledgements I am using this opportunity to express my gratitude to everyone who sup- ported me throughout the Master of Engineering course. I am thankful for their aspiring guidance, invaluable constructive criticism, and friendly advice during the project work. I am sincerely grateful to them for sharing their truthful and illuminating views on a number of issues related to the project. Firstly, I would like to express my sincere gratitude to my supervisor Prof. Wen Changyun for guiding me in the thesis writing and helping me in all the time of thesis submission. I would also like to thank Prof. Er Meng Joo for providing me this great opportunity to pursue Masters in the prestigious NTU Singapore. I would like to thank him for the continuous support of my Masters' study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and other career matters. I could not have imagined working on this research project without his motivation and appreciation towards my research topic. He gave me the freedom to choose my research topic according to my interest and continuously guided me to work in the right direction. Besides my supervisor, I would like to thank the rest of my friends and seniors in NTU for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives. Last but not least, I would like to thank my brother who supported me all throughout my research and encouraged me to work in the right direction. My sincere thanks also go to my parents who made it possible for me to grab this opportunity and never missed a chance to encourage me and support me in any manner possible. vii viii Abstract When learning any form of classical music vocals without a teacher, even subtle deviations can lead to wrong training, which can be difficult to remedy in the future. In this research work, a real-time tool has been developed to deal with this situation by assisting people in learning Indian classical music. This tool will have a set of pre-defined Swaras, Alankaras, and Ragas (Indian classical music concepts). Users can practice any musical piece from this set and the tool will inform them of the mistakes they make, by smartly matching their voice with the dynamically defined pattern. Users are free to sing in any given scale, which they define in the beginning by singing the root note of their preferred scale. Using the PredominantPitchMelodia algorithm, the tool identifies the pitch values of the musical piece. From these pitch values, the tool identifies the root-note and set it as a reference. This root-note defines the scale of the user's voice. Further, for identifying the simplest basic musical piece, the k-means clustering method has been implemented along with some in- dex rearrangement. To identify any general musical piece, moving average, gaussian filter and step-detection algorithm have been implemented along with prominent step-filtering method. Voice stability and Pitch accuracy have been proposed to serve as the evaluation criteria. The functioning of the tool is testified on a varied range of audio samples, namely, male and female voices, Ukulele and Harmonium audio samples and existing musical instrument tuning applications like GuitarTuna, Ukulele Tuner, and so on have been used as the ground truth for the verification. For future work, the just intonation tuning method will be implemented as this method is mostly used in Indian classical music. Also, the tool can be formulated for different cultural music (western music, Chinese music, folk music etc.), medical purposes (brain-computer interface, neural disorders like schizophrenia etc.), meditation and for music composition. ix x List of Figures 3.1 Pitch values of audio signal (of root note) obtained using Pre- dominantPitchMelodia. 22 3.2 k-means clustering to identify the root note. 23 3.3 Pitch values of Alankara audio signal obtained using Predom- inantPitchMelodia. 24 3.4 k-means clustering to identify the combination of notes present in Alankara............................. 25 3.5 Clusters of n notes to obtain sequence of notes and their com- parison with the ideal range of notes for calculating voice sta- bility and pitch accuracy. 26 3.6 Moving average to smoothen the signal. 27 3.7 Moving average to smoothen the signal. 28 3.8 Implementation of Gaussian Filter to identify the steps. 29 3.9 Implementation of Threshold to identify the prominent steps. 30 xi xii List of Tables 4.1 Results showing Voice stability and number of notes in ideal range for 5 subjects of basic alankara . 37 4.2 Notes in range for 5 subjects of basic alankara . 37 4.3 Pitch Accuracy of 5 subjects in Hz for basic alankara . 38 4.4 Results showing Voice stability and number of notes in ideal range for 5 subjects of general alankara . 39 4.5 Results showing Voice stability and number of notes in ideal range for instrumental recordings of general alankara . 39 4.6 Pitch Accuracy of 5 subjects in Hz for general alankara . 41 4.7 Notes in range for 5 subjects of general alankara . 42 4.8 Pitch Accuracy of two different scales of Harmonium in Hz . 43 4.9 Pitch Accuracy of two different scales of Flute in Hz . 44 xiii xiv Contents Acknowledgements . vii Abstract . ix List of Figures . xi List of Tables . xiii 1 Introduction 1 1.1 Motivation . .1 1.2 Objectives . .2 1.3 Background . .3 1.4 Contributions . .5 1.5 Organization of the Thesis . .6 2 Literature Review 9 2.1 Related Works . .9 2.1.1 Music Theory . 11 2.1.2 Vocal Melody Extraction . 13 2.1.3 Tonic Identification . 14 2.1.4 Pattern Recognition . 15 2.1.5 Tuning Method . 16 2.1.6 Music Performance assessment . 17 2.2 Challenges . 19 3 Design and Development 21 3.1 Root-note Identification . 21 3.2 Basic Alankara Identification . 24 3.3 General Alankara Identification . 26 xv 3.3.1 Moving Average . 28 3.3.2 Step-detection . 29 3.4 Summary . 30 4 Results and Discussion 33 4.1 Ideal Range Calculation . 33 4.2 Evaluation Criteria . 34 4.2.1 Notes in Range . 34 4.2.2 Voice Stability . 35 4.2.3 Pitch Accuracy . 35 4.3 Experimental Results . 36 4.3.1 Basic Alankara . 36 4.3.2 General alankara . 38 4.4 Summary . 44 5 Conclusions and Future Extensions 47 5.1 Conclusions . 47 5.2 Future Work . 48 5.3 Applications . 49 Author's Publication(s) . 51 Bibliography . 53 xvi Chapter 1 Introduction Indian classical music has for the past 50 years become an increasingly inter- national phenomenon. A great number of people have had an urge to learn this form of music but lacked resources that could direct them in the right direction. This problem presents an opportunity for a vocal tutor system that can suggest the user improve their vocal skills and provide a detailed analysis of their vocal ability. This vocal tutor can smartly guide the user in practicing classical music on their own and it can be used for music of different cultures in the future.