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.
Author(s): Ambuja Salgaonkar, Makarand Velankar
Series: Advances in Intelligent Systems and Computing, 1444
Publisher: Springer
Year: 2023
Language: English
Pages: 242
City: Singapore
Foreword
Preface
Contents
About the Editors
Computer Assisted Musicology
Bridging the Gap Between Musicological Knowledge and Performance Practice with Audio MIR
Introduction
Raga Grammar
Audio Signal Processing
Distributional and Structural Representations
Conclusion
References
Spectral Analysis of Voice Training Practices in the Hindustani Khayāl
Introduction
Voice Science and Psychoacoustics: Brief Review of Recent Work
The Non-linear Source-Filter Model, Harmonics and Formants
Interpreting the Spectral Envelope
Case Study: Examining Aspects of Kumar Gandharva’s Pedagogy of the Voice
A Brief Survey of Kumar Gandharva’s Timbral Aesthetics
Mukhabandī—Closed Pronunciation as a Pedagogical Tool
Conclusions and Further Research
References
Software Assisted Analysis of Music: An Approach to Understanding Rāga-s
Introduction
The Rāga
Melodic Perception and the Rāga
Graphical Depiction of Melodic Contour
The Rāga-S
Mārvā, Pūriyā and Sohanī
The Prescriptive
Analysis of the Performances of Marvā, Pūriyā and Sohanī
Performances of Marvā
Performances of Pūriyā
Performance of Sohanī
Bhūpālī and Deśkār
The Prescriptive
Analysis of the Performances of Bhūpalī and Deśkār
Performances of Bhūpālī
Performance of Deśkar
Conclusion
Notation Employed
References
Machine Learning Approaches to Music
Music Feature Extraction for Machine Learning
Introduction
Literature Survey
Feature Selection
Distinct Feature Set (DFS)
Relevant Feature Set (RFS)
Subset Evaluator Feature Set (SEFS)
Machine Learning Models
Results and Discussions
Conclusion and Future Directions
References
Role of Prosody in Music Meaning
Background
Parameters of Music Contributing to Prosody
Tempo and Rhythm
Instrumentation
Melody
Discussion and Suggestions
Conclusion
References
Estimation of Prosody in Music: A Case Study of Geet Ramayana
Background
Literature Survey
Extraction of Prosodic Information from a Musical Piece
Study of Prosody in Geet-Ramayana
Conclusion
References
Raga Recognition Using Neural Networks and N-grams of Melodies
Introduction and Background
Material and Methods
Data
Results
Discussion
Conclusion
References
Developing a Musicality Scale for Haiku-Likes
Preamble
Literature Survey
Evolution of HCM: Prabandha to Haiku-Gaan
Bandish, Musicality and Haiku in Indian Languages
Materials and Methods
A Confirmatory Test and Conclusions
Conclusion
References
Composition and Choreography
Composing Music by Machine Using Particle Swarm Optimization
Introduction
Composing Music Using Tabla
Particle Swarm Optimization (PSO)
Methodology
Results and Discussion
Conclusion
References
Computable Aesthetics for Dance
Introduction
Previous Work
Biology, Neurology
Learning from Data
Learning the Grammar
The Flow: The Learning and Automation Loop
Universals from Neuroscience: Ramachandran's ``Navarasas'' or 9 Aesthetic Sources
Grouping
Peak Shift
Contrast
Isolation
Peekaboo, or Perceptual Problem Solving
Abhorrence of Coincidences
Orderliness
Symmetry
Metaphor
Discussion: BharataNatyam, the Navarasas and Computation
Grouping, Contrast and Symmetry
Peak Shift, Isolation, Abhorrence of Coincidences
Perceptual Problem-Solving and Metaphor
Discussion
Conclusion
Future Work and Its Rationale
References
Design and Implementation of a Computational Model for BharataNatyam Choreography
Introduction and Literature Review
Structure of BharataNatyam
Problem Definition
Data Modelling
Generation of New Dance Poses
Genetic Algorithm: Optimal Is Aesthetic
Classification: High Dimension and Reduction
Fractal Dimension for Aesthetics
Generating N-Beat Dance Poses for Choreography
The ArttoSMart Interface
Conclusions
References
Interfacing the Traditional with the Modern
Automatic Mapping of BharatNAtyam Margam to Sri Chakra Dance
Glimpses of Research in Dance Making
Sri Chakra Diagram and SC Dance
Chakra-Philosophy
SC Dance Nomenclature
BNM and SC
Drawing the SC
Algorithm DrawSC
Traversal of SC for BNM
Theorem: There Exists a Mapping of BNM with SCG
Defining a Walk in SCG to Map it BNM
Confirmation of our Logic of Mapping BNM to SGC
Further Possibilities
Conclusion
References
Computation of 22 Shrutis: A Fibonacci Sequence-Based Approach
Terminology and Shruti Computations Using Swaymbhu Gandhaar
Contentions with Mathematical Back Up
Conclusion
References
Signal Processing in Music Production: The Death of High Fidelity and the Art of Spoilage
Introduction
Aesthetics of Spoilage
Aesthetics of Music Production
The Neutral Unprocessed Sound (Hi-Fi)
The Classic Sound
The Fantasia
Analogue Approaches to Signal Processing
Digital Approaches to Signal Processing
Inverse Domains
Time-Domain Processing
Frequency-Domain Processing
Both Domains Together
Conclusion
References
Computational Indian Musicology: Challenges and New Horizons
Introduction
Current Status in India
Computational Indian Musicology
Computational Music Theory and Analysis
Computational Historical Musicology
Computational Ethnomusicology
Computational Cognitive Musicology
Computational Performance Research
Challenges and the Way Forward
References
