Modeling a Raga-Based Song and Evaluating Its Raga Content: Why It Matters in a Clinical Setting

Tewari, Swarima; Chakraborty, Samarjit

doi:10.1007/978-981-13-8222-2_21

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The strategy has been successfully implemented in the context of patients with cerebrovascular accident and diffuse head injury [8]. Therefore, evaluating the raga content in the song becomes a research problem [9].…”

Section: Measuring Raga Content Using Correlation Coefficientmentioning

confidence: 99%

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An Improved Method of Raga Content Assessment in Songs with Therapeutic Benefits

Tewari¹,

Chakraborty²

2020

JAMC

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A raga, in Indian classical music, be it Hindustani or Carnatic, is a melodic structure with fixed notes and a set of rules that characterize a particular mood conveyed by performance. Ragas have well acknowledged rich emotional content (the so called raga rasa) but a common man generally lacks the aesthetic sense to understand these ragas and hence cannot derive their therapeutic benefits completely. Accordingly when it comes to music medicine and music therapy, a nice idea would be to first render songs based on a particular raga one by one with gradually increasing raga content in them and then render this specific raga itself whereby determining the raga content in a song becomes an interesting and intriguing research problem. In an earlier work, we proposed a technique relying on correlation coefficient (based on pitch that characterize a musical note) of melodic shapes, as a measure of melodic similarity, to address the issue. Considering the limitations of correlation coefficient, in the present work, we propose an improved method by incorporating more features into our analysis. These additional features include note duration, inter onset interval and pitch movements between the notes. The experimental results are encouraging.

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Section: Measuring Raga Content Using Correlation Coefficientmentioning

confidence: 99%

“…Spearman's rank correlation coefficient is calculated by the formula ρ = 1 -6∑d 2 /n(n 2 -1) where d = X -Y. The significance can be tested in the same manner as in the case of r replacing r by ρ and performing a t-test as described previously [9].…”

Section: Moving Towards An Improved Measure Of Raga Contentmentioning

confidence: 99%

An Improved Method of Raga Content Assessment in Songs with Therapeutic Benefits

Tewari¹,

Chakraborty²

2020

JAMC

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“…But they ignore temporal aspects which are useful in distinguishing ragas which have same set of notes [9]. In [10], [11] have used 'chromogram' of ragas feature and Baysian classifier, random forest and K-star for identification. In [12]- [19] authors have made use of arohana-avarohana pattern to capture the sequential information in the melody.…”

Section: Introductionmentioning

confidence: 99%

Raga classification based on pitch co-occurrence based features

Rajadnya

Joshi²

2021

IJEECS

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<p><span>Analysis and classification of raga is the need of time especially in music industry. With the presence of abundance of multimedia data on internet, it is imperative to develop appropriate tools to classify ragas. In this work, an attempt has been made to use occurrence pattern of pitch based svara (note) for classification. Sequence of notes is an important cue in the raga classification. Pitch based svara (note) profile is formed. This pattern presents in the signal along with its statistical distribution can be characterized using co-occurrence matrix. Proposed note co-occurrence matrix summarizes this aspect. This matrix captures both tonal and temporal aspects of melody. Ragas differ in terms of distribution of spectral power. K-nearest neighbor (KNN) has been used as the classifier. Publicly available database consisting of 300 recordings of 30 Hindustani ragas consisting of 130 hours of audio recordings stored as 160 kbps mp3 fileswhich is part of CompMusic project is used. Leave one out validation strategy is used to evaluate the performance. Experimental result indicates the effectiveness of the proposed scheme which is giving accuracy of 93.7%.</span></p>

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Raga Classification Based on Novel Method of Pitch Co-Occurrence

Rajadnya¹,

Joshi²

2022

IJRTE

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Automatic identification of raga is a growing research area and has captured significant attention from movie making industry. It is the need of time to develop efficient tools for data mining the vast audio visual data on internet. In particular, to search for a specific raga. Applications of raga search are in musicological studies, similarity based search. Ascending and descending pattern of swaras is an important feature in the raga classification. Pitch tracks of swaras are obtained from raw audio recordings. This research has utilised the pattern developed due to co-occurrence of pitches of swaras for classification. This pattern gives a concise representation of the signal which contains time and frequency information of the raga. K Nearest Neighbour (KNN) has been used as the classifier.

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Modeling a Raga-Based Song and Evaluating Its Raga Content: Why It Matters in a Clinical Setting

Cited by 3 publications

References 8 publications

An Improved Method of Raga Content Assessment in Songs with Therapeutic Benefits

An Improved Method of Raga Content Assessment in Songs with Therapeutic Benefits

Raga classification based on pitch co-occurrence based features

Raga Classification Based on Novel Method of Pitch Co-Occurrence

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