Abstract
Music is a special set of sounds arranged in ways that can express different human emotions, feelings, thoughts, situations, and times. Throughout the world, several researchers have engaged themselves to retrieve this information present in different musics. In the present work, the Sandhi Prakash ragas, based on their time of presentation (dawn or dusk) have been classified. The audio rendering of Sandhi Prakash ragas is taken from Hindustani vocal music. The line spectral frequency (LSF)-based feature extraction technique has been used. The generated feature vector is then evaluated through several classifiers like MLP, RF, LibSVM, Naive Bayes, Bays Net, SMO, Simple Logistics, and achieved 99.86% recognition accuracy by MLP and RF classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farishta, A., Rathod, P.P., Barbole, S., Belkhede, P.: Artificial Neural Network to identify Indian classical music raga’s. Int. J. Future Gener. Commun. Network. 13(2s), 106–112 (2020)
Lele, J.A., Abhyankar, A.S.: Towards Raga identification of hindustani classical music. In: 2019 IEEE Pune Section International Conference, pp. 1–4. IEEE (2019)
Anand, A.: Raga identification using convolutional neural network. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms, pp. 1–6. IEEE (2019)
Gulati, S., Serra, J., Ishwar, V., Sentürk, S., Serra, X.: Phrase-based rāga recognition using vector space modeling. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 66–70. IEEE (2016)
Ranjani, H.G., Paramashivan, D., Sreenivas, T.V.: Discovering structural similarities among rāgas in Indian Art Music. Comput. Approach Sādhanā 44(5), 120 (2019)
Bidkar, A.A., DeshPande, R.S., Dandawate, Y.H.: A novel approach for selection of features for North Indian classical raga recognition of instrumental music. In: 2018 International Conference On Advances in Communication and Computing Technology ICACCT, pp. 499–503. IEEE (2018)
Gulati, S., Serrà, J., Ganguli, K. K., Sentürk, S., Serra, X.: Time-delayed melody surfaces for raga recognition. In: Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pp. 751–757, New York, USA (2016)
Dandawate, Y.H., Kumari, P., Bidkar, A.: Indian instrumental music: Raga analysis and classification. In: 1st international conference on next generation computing technologies (NGCT), pp. 725–729. IEEE (2015)
Katte, T., Tiple, B.S.: Techniques for Indian classical raga identification-a survey. In: 2014 Annual IEEE India Conference (INDICON), pp. 1–6 (2014)
Katte, T.: Multiple techniques for raga identification in Indian classical music. Int. J. Electron. Comput. Eng. 4(6), 82–7 (2013)
Sharma, A.K., Lakhtaria, K.I., Panwar, A., Vishwakarma, S.: An analytical approach based on self organized maps (SOM) in Indian classical music raga clustering. In: 2014 Seventh International Conference on Contemporary Computing (IC3), pp. 449–453 IEEE (2014)
Kirthika, P., Chattamvelli, R.: A review of raga based music classification and music information retrieval (MIR). In: 2012 IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA), pp. 1–5. IEEE (2012)
Belle, S., Joshi, R., Rao, P.: Raga identification by using swara intonation. J. ITC Sangeet Res. Acad. 23(3) (2009)
Alim, S.A., Rashid, N.K.A.: Some commonly used speech feature extraction algorithms. In: From Natural to Artificial Intelligence-Algorithms and Applications (2018)
Itakura, F.: Line spectrum representation of linear predictor coefficients of speech signals. J. Acoust. Soc. Am. 57(S1), S35–S35 (1975)
Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Phadikar, S., Roy, K.: Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. Int. J. Speech Technol. 21(4), 753–760 (2018)
Mukherjee, H., Ganguly, S., Phadikar, S. and Roy, K.: An ensemble learning based bangla phoneme identification system using LSF-G features. In: Advanced Computational and Communication Paradigms, pp. 207–215. Springer, Singapore (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Basu, D., Mukherjee, H., Sen, S., Roy, K. (2022). Identification of the Dawn or Dusk Ragas. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore. https://doi.org/10.1007/978-981-16-5207-3_49
Download citation
DOI: https://doi.org/10.1007/978-981-16-5207-3_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5206-6
Online ISBN: 978-981-16-5207-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)