Automatic Synthesis of Notes Based on Carnatic Music Raga Characteristics

Varadharajan, Janani; Sridharan, Guruprasad; Natarajan, Vignesh; Ramaswamy, Sridhar

doi:10.1007/978-3-319-07353-8_17

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…To the best of our knowledge, all the existing works on gamaka analysis focus on acoustic data for this task [8]- [12]. Other works done on Indian Classical Music (ICM) such as automatic music composition, singing voice synthesis and rāga analysis do not address the problem of predicting gamakas from symbolic music [13]- [25]. In Western music, the studies performed on detection and analysis of ornamentations as well as expressive music performance use audio data for these tasks [26]- [28].…”

Section: Introductionmentioning

confidence: 99%

Predicting $Gamaka$ s—The Essential Embellishments in Karnatic Music

2019

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Gamakas are the musical embellishments used in Karnatic Music. Predicting them from the musical notations plays an important part in applications like automatic synthesis and composition of Karnatic Music. Since there are no well-defined rules governing the use of gamakas, predicting them is a challenging problem. In this work, we propose a method to detect the presence and type of gamakas, in a datadriven manner, from the annotated symbolic music alone. We propose features based on the notes of the song for these tasks. These features are used as inputs to a Random Forest Classifier. We digitise 80 songs from a well known reference book of Karnatic music to create a dataset consisting roughly 30000 notes. We train the classifier on around 12000 notes and test on roughly 18000 notes. From our experiments, the accuracy values obtained for predicting gamaka presence and type are ∼77% and ∼70%, respectively. These are significantly better than random classification accuracies. We also analyse the importance of neighbourhood of notes for the detection and classification of gamakas. It is observed that the best accuracy is obtained for gamaka presence detection when a both-sided neighbourhood of size three is considered; and best accuracy for gamaka type prediction is obtained with a both-sided neighbourhood of size one. The analysis performed on the training data reveals that there is information contained in these neighbourhoods for distinguishing between gamaka and non-gamaka notes. INDEX TERMS Gamaka, karnatic music, note-based features, random forest classifier, symbolic music.

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Section: Introductionmentioning

confidence: 99%

Predicting $Gamaka$ s—The Essential Embellishments in Karnatic Music

2019

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“…In the context of Indian Classical Music, multiple works have addressed gamaka identification from raw audio signal [12,13]. Other research efforts on the automatic music composition [14,15,16,17] and singing voice synthesis [18] for Indian Classical Music do not address the problem of predicting gamakas from the score files.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Aesthetic Elements in Karnatic Music: A Machine Learning Approach

Vijayakumar

Vijayasenan

2018

Interspeech 2018

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Gamakas, the embellishments and ornamentations used to enhance musical experience, are defining features of Karnatic Music (KM). The appropriateness of using gamaka is determined by aesthetics and is often developed by musicians with experience. Therefore, understanding and modeling gamaka is a significant bottleneck in applications like music synthesis, automatic accompaniment, etc. in the context of KM. To this end, we propose to learn both the presence and the type of gamaka in a data-driven manner using annotated symbolic music. In particular, we explore the efficacy of three classes of featuresnote-based, phonetic and structural-and train a Random Forest Classifier to predict the existence and the type of gamaka. The observed accuracy is ∼70% for gamaka detection and ∼60% for gamaka classification. Finally, we present an analysis of the features and find that frequency and duration of the neighbouring notes prove to be the most important features.

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Fisher-Jenks Classification Technique for Music Raaga Identification

B D,

Kiran

2023

2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)

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Automatic Synthesis of Notes Based on Carnatic Music Raga Characteristics

Cited by 4 publications

References 1 publication

Predicting $Gamaka$ s—The Essential Embellishments in Karnatic Music

Predicting $Gamaka$ s—The Essential Embellishments in Karnatic Music

Prediction of Aesthetic Elements in Karnatic Music: A Machine Learning Approach

Fisher-Jenks Classification Technique for Music Raaga Identification

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