Raga identification from Hindustani classical music signal using compositional properties

Sarkar, Rajib; Naskar, Soumya Kanti; Saha, Sanjoy Kumar

doi:10.1007/s00791-017-0282-x

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…The results in [7] showed the model required varied duration of time for better accuracy but the results were not enough for identifying the raga accurately. 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.…”

Section: Discussionmentioning

confidence: 99%

“…Sarkar et al [7] developed raga identification from Hindustani Classical Music signal based on the compositional properties. In the existing methods, identification of raga automatically utilized more time and therefore, to overcome such an issue, the developed model introduced a co-occurrence matrix for summarizing the problem.…”

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%

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

Gowrishankar¹,

Nagappa²

2021

Atlantis Highlights in Computer Sciences

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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 %.

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Section: Discussionmentioning

confidence: 99%

Section: Resolution Of Pitch-classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gowrishankar¹,

Nagappa²

2021

Atlantis Highlights in Computer Sciences

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“…Rajib Sarkar [10] developed raga identification from Hindustani classical music signals using compositional properties. The developed model introduced a co-occurrence matrix for summarizing the mood aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Krishnaiah¹,

Parameshachari²

2021

IJIES

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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 %.

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“…Sarkar et al [20] utilized Hindustani and Carnatic classical music for raga identification from the audio signals for identifying the properties. The existing models performed an automated raga recognition that consumed time and overcame the problem.…”

Section: Introductionmentioning

confidence: 99%

Raga classification using enhanced spatial bound whale optimization algorithm

Gowrishankar

Bhajantri

2023

IJEECS

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A raga is a unique set of notes with certain rules that carefully followed, retain and protect its purity and produce amazing musical effects. An automated raga transcription and identification is important for computational musicology, which is an important step for musicology for indexing, classifying, and recommending tunes. In the present research, the audio features such as mel frequency cepstrum coefficients (MFCCs), spectral flux, short time energy, audio feature extractor, and spectral centroid features are used for the prediction of a raga. The model showed more complexity which means it required lots of training data. The proposed enhanced spatial bound whale optimization algorithm (ESBWOA) is used that overcome the feature selection problem of high dimensional features. In addition to this, a weighted salp swarm algorithm (SSA) is used for selecting the tone-based features from the ragas based on amplitude or each raga sample. The features were fed for bidirectional long short-term memory (Bi-LSTM) network, which enhanced the success rate for raga identification and classification. The present research uses CompMusic dataset in the research work where 9 classes for Carnatic music and 7 classes in Hindustani music are considered for the classification of ragas.

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Raga identification from Hindustani classical music signal using compositional properties

Cited by 7 publications

References 7 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

Raga classification using enhanced spatial bound whale optimization algorithm

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