Computer Assisted Music and Dramatics

1. Front Matter

   Pages i-xvi

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2. Computer Assisted Musicology

   1. Front Matter

      Pages 1-1

      PDF

   2. Bridging the Gap Between Musicological Knowledge and Performance Practice with Audio MIR

      • Preeti Rao

      Pages 3-13

   3. Spectral Analysis of Voice Training Practices in the Hindustani Khayāl

      • Srijan Deshpande

      Pages 15-27

   4. Software Assisted Analysis of Music: An Approach to Understanding Rāga-s

      • Sandeep Bagchee

      Pages 29-56

3. Machine Learning Approaches to Music

   1. Front Matter

      Pages 57-57

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   2. Music Feature Extraction for Machine Learning

      • Makarand Velankar, Parag Kulkarni

      Pages 59-70

   3. Role of Prosody in Music Meaning

      • Hari Sahasrabuddhe

      Pages 71-76

   4. Estimation of Prosody in Music: A Case Study of Geet Ramayana

      • Ambuja Salgaonkar, Makarand Velankar

      Pages 77-91

   5. Raga Recognition Using Neural Networks and N-grams of Melodies

      • Ashish Sharma, Ambuja Salgaonkar

      Pages 93-109

   6. Developing a Musicality Scale for Haiku-Likes

      • Ambuja Salgaonkar, Anjali Nigwekar, Atindra Sarvadikar

      Pages 111-130

4. Composition and Choreography

   1. Front Matter

      Pages 131-131

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   2. Composing Music by Machine Using Particle Swarm Optimization

      • Siby Abraham, Subodh Deolekar

      Pages 133-147

   3. Computable Aesthetics for Dance

      • Sangeeta Chakrabarty, Ramprasad S. Joshi

      Pages 149-164

   4. Design and Implementation of a Computational Model for BharataNatyam Choreography

      • Sangeeta Chakrabarty

      Pages 165-175

5. Interfacing the Traditional with the Modern

   1. Front Matter

      Pages 177-177

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   2. Automatic Mapping of BharatNAtyam Margam to Sri Chakra Dance

      • Ambuja Salgaonkar, Padmaja Venkatesh Suresh, P. M. Sindhu

      Pages 179-203

   3. Computation of 22 Shrutis: A Fibonacci Sequence-Based Approach

      • Ambuja Salgaonkar

      Pages 205-220

   4. Signal Processing in Music Production: The Death of High Fidelity and the Art of Spoilage

      • David Courtney

      Pages 221-229

   5. Computational Indian Musicology: Challenges and New Horizons

      • Vinod Vidwans

      Pages 231-239

This book is intended for researchers interested in using computational methods and tools to engage with music, dance and theatre. The chapters have evolved out of presentations and deliberations at an international workshop entitled Computer Assisted Music and Dramatics: Possibilities and Challenges organized by University of Mumbai in honour of Professor Hari Sahasrabuddhe, a renowned educator and a pioneering computational musicologist (CM) of Indian classical music.

The workshop included contributions from CM as well as musicians with a special focus on South Asian arts. The case studies and reflective essays here are based on analyses of genres, practices and theoretical constructs modelled computationally. They offer a balanced and complementary perspective to help innovation in the synthesis of music by extracting information from recorded performances. This material would be of interest to scholars of the sciences and humanities and facilitate exchanges and generation of ideas.

• Acoustics and Engineering of Music
• Music Information Retrieval
• Machine learning for Compositions
• Automatic Scripting and Transcripting
• Augmented Musical Instruments
• Fine arts and NLP
• Music, Dramatics and Education Models
• Cognitive Science for Sound and Music
• Sound and Music in Visual Media
• Simulation of Motion and Gestures

• Computer Science, University of Mumbai, Mumbai, India

  Ambuja Salgaonkar

• Information Technology, MKSSS’s Cummins College of Engineering, Pune, India

  Makarand Velankar

Ambuja Salgaonkar has a Ph.D. in Computer Science, M.B.A. (Operations Mgmt), M.A. (English Lit) and about 30 years’ experience teaching at university level and research in problem solving using AI. She has extensively researched Indian heritage science for contemporary applications, including Katapayadi numbers and Shree yantra type of designs for information retrieval and security, exploring the syntax of the Indus script as a consistent messaging system and image processing of palm leaf manuscripts. Her recent published work is in motion planning for agricultural robots, automatic question generation and Konkani-Hindi machine translation. She has been coordinating corpus generating activities related to Marathi and its dialects for the Bhashini project of Government of India. She is the Marathi translator of a collection of 49 essays with the title “India’s cultural history up to 1947 CE,” a multi-lingual project with international collaborators. Ambuja has been a student ofIndian classical music (violin). She has been fortunate to receive guidance from Professor Hari Sahasrabuddhe for her various researches including her Ph.D. work. She is a prolific writer in Marathi. Her articles on Vidushi Veena Sahasrabuddhe, Vidushi Sushilarani Patel and Dr Anjali Nigwekar have been well received. Transcreation of Alice in Wonderland, translation of Tagore's Geetanjali and Haiku forms of Kabir's Dohas are her contributions. Ambuja's current passion is educational technology. She was instrumental in designing as many as ten courses to teach Indian classical music in distance and open learning mode. She successfully conducted a three-semester specialization in computer-assisted music learning at University of Mumbai. Creation of a specialized MOOC on conjoining Ravindra Sangeet with Hindustani classical music and developing a scale for measuring the complexity of composition are her dream projects. 

Makarand Velankar, M.E., Ph.D. in ComputerEngineering from SP Pune University, has about 11 years of industry experience. Later, he joined MKSSS’s Cummins College of Engineering for Women, Pune, and has been teaching there for the last 21 years. His passion for research in computational musicology, developed through interactions with Professor Sahasrabuddhe, has led him to explore the world music canvas with focus on Indian music. His doctoral research on query by humming, content-based retrieval, modelling melodic similarity, sentiment analysis, performance evaluation and ML-based recommendation system has received appreciation in conferences like ISMIR. His work in this domain has been published in reputed journals. Developing a commercially available personalized music recommendation system is his immediate goal. In recent times, he has been engaged in exploring the domain of automatic generation of music.

Makarand’s passion for entrepreneurship led him to become a start-up mentor for Wadhwani AI, a multinational NGOlocated in Mumbai. So far, he has mentored more than five student start-ups and provided consultancy to two established business setups to scale up. He has been heading a pre-incubation centre at his college. He has also initiated and nurtured a music technology group.

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