Melodic pattern recognition in Indian classical music for raga identification

Velankar, Makarand; Deshpande, Amod; Kulkarni, Parag

doi:10.1007/s41870-018-0245-6

Cited by 11 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was due to less number of raga samples utilized in the research and obtained accuracy of 91.66 % and Error value of 8.34. The developed model results obtained by [8] utilized various audio features were mathematically analyzed considered the ground truth values. But for amplitude-based features limited the performance of the model and obtained accuracy ranging from 49 to 63% and Standard deviation of 0.037 The developed model results from [9] were obtained based on characteristics of the dataset-required size, class balance, quality of the annotations required improvement for achieving good performance in terms of accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Makarand Velankar et al [8] performed pattern recognition of the tone melodies in ICM for raga identification. From the analysis done on the existing methods, the researchers have taken initiative for identifying raga with the least sample duration.…”

Section: Resolution Of Pitch-classesmentioning

confidence: 99%

“…The audio features are used for categorizing the low-level highlights and midlevel highlights. The low-level highlights are extricated by utilizing different sign preparing procedures while the mid-level highlights are removed on the low-level highlights [7,8]. There were various researches undertaken for automatic raga recognition as it is helpful for many applications such as automated note transcription, recommendation of music, classification, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Gowrishankar¹,

Nagappa²

2021

Atlantis Highlights in Computer Sciences

View full text Add to dashboard Cite

Raga recognition is only possible by trained musician to understand the notes based on the lead voice but a beginner is unable to decode the notes. This is significant for current scenarios in developing an automated note transcription system in Indian Classical Music (ICM). In the present research, various properties of raga and the machine learning techniques that are used for identifying the raga by a machine rather than a human or music expert are surveyed. The previously developed automatic raga recognition techniques using Carnatic and Hindustani Music, the main drawbacks and the improvements required are discussed. The present research work discusses about the future proposed models for automatic raga recognition using pitch detection algorithm, finding Tuning Offset, and Note Segmentation process. The proposed model will obtain better accuracy more than 96 % when compared to the existing CNN, GMM that obtained accuracy of 94 % and 95 %.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Resolution Of Pitch-classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Gowrishankar¹,

Nagappa²

2021

Atlantis Highlights in Computer Sciences

View full text Add to dashboard Cite

show abstract

“…Makarand Velankar [11] developed melodic pattern recognition in Indian classical music for raga identification. The developed model extracted the pitch-based features as it was used for identifying the melodic level using the autocorrelation method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Where, is the linear equation of unknown parameters represented as 1 … , expressed in Eqs. (11) and (12).…”

Section: ≪ ≪mentioning

confidence: 99%

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Krishnaiah¹,

Parameshachari²

2021

IJIES

View full text Add to dashboard Cite

Automatic Raga identification is an important step in computational musicology as it is helpful for indexing Indian music, classifying, recommending tunes, etc. However, automatic raga recognition is a difficult task as it requires guidance from the expertise training and each of the ragas is identified based on the characteristic phrase and pitch collection. However, the identification or extraction of appropriate musical sample features was difficult to enhance the success rate of the classifier. The feature identification and extraction were considered as the major issue in the existing models. To overcome such an issue, the proposed hybrid spectral feature extraction technique extracted and combined the spectral features of audios such as Spread, centroid, skewness, Mel Frequency Cepstrum Coefficients (MFCCs) and Linear Predictor Coefficients (LPCs) reduced the dimensionality complexity enhanced the success rate of the Multi-Support Vector Machine (MSVM) classifier for mood classification. The CompMusic dataset is utilized for this research work, where 4 types of ragas namely Sindhu Bhairavi, Darbari, Saveri, and Sri Raga, are considered for the mood classification of ragas. From these ragas, tone based features are extracted based on the amplitude of each raga samples and are fed to the MSVM classifier for mood classification of 4 different types are Sympathy, Serious, Peace, and Sad. The classification results for the proposed hybrid spectral feature extraction obtained better accuracy of 97.53% when compared to the existing Hidden Markov Model (HMM) obtained accuracy as 95.3%, Convolution Neural Network (CNN) model as 94%, and Gaussian Mixture Model (GMM) as 95 %.

show abstract

Computational Approaches for Indian Classical Music: A Comprehensive Review

Singh

Biswas²

2022

Advances in Speech and Music Technology

View full text Add to dashboard Cite

Melodic pattern recognition in Indian classical music for raga identification

Cited by 11 publications

References 9 publications

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Computational Approaches for Indian Classical Music: A Comprehensive Review

Contact Info

Product

Resources

About