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.

• Part I: Computer Assisted Musicology.- Bridging the Gap between Musicological Knowledge and Performance Practice with Audio MIR.- Spectral Analysis of Voice Training Practices in the Hindustani Khayāl.- Software Assisted Analysis of Music: An Approach to Understanding Rāga-s.- Part II: Machine Learning Approaches to Music.- Music Feature Extraction for Machine Learning.- Role of Prosody in Music Meaning.- Estimation of Prosody in Music: A case study of Geet Ramayana.- Raga Recognition Using Neural Networks and N-grams of Melodies.- Developing a musicality scale for Haiku-likes.- Part III: Composition and Choreography.- Composing Music by Machine using Particle Swarm Optimization.- Computable Aesthetics for Dance.- Design and Implementation of a Computational Model for BharataNatyam Choreography.- Part IV: Interfacing the Traditional with the Modern.- Automatic Mapping of BharatNatyam Margam to Sri Chakra Dance.- Computation of 22 Shrutis: A Fibonacci Sequence-Based Approach.- Signal Processing In Music Production: The Death Of High Fidelity And The Art Of Spoilage.- Computational Indian Musicology: Challenges and New Horizons.

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.

• Part I: Computer Assisted Musicology.- Bridging the Gap between Musicological Knowledge and Performance Practice with Audio MIR.- Spectral Analysis of Voice Training Practices in the Hindustani Khayāl.- Software Assisted Analysis of Music: An Approach to Understanding Rāga-s.- Part II: Machine Learning Approaches to Music.- Music Feature Extraction for Machine Learning.- Role of Prosody in Music Meaning.- Estimation of Prosody in Music: A case study of Geet Ramayana.- Raga Recognition Using Neural Networks and N-grams of Melodies.- Developing a musicality scale for Haiku-likes.- Part III: Composition and Choreography.- Composing Music by Machine using Particle Swarm Optimization.- Computable Aesthetics for Dance.- Design and Implementation of a Computational Model for BharataNatyam Choreography.- Part IV: Interfacing the Traditional with the Modern.- Automatic Mapping of BharatNatyam Margam to Sri Chakra Dance.- Computation of 22 Shrutis: A Fibonacci Sequence-Based Approach.- Signal Processing In Music Production: The Death Of High Fidelity And The Art Of Spoilage.- Computational Indian Musicology: Challenges and New Horizons.