Open Access
Research Article
Artificial intelligence-based classification performance evaluation in monophonic and polyphonic indian classical instruments recognition with hybrid domain features amalgamation
*Abhijit ChitreCorresponding authorabhijit.chitre@viit.ac.inDepartment of Electronics and Telecommunication Vishwakarma Institute of Information TechnologyDepartment Electronics and Telecommunication Engineering Vishwakarma Institute of Information TechnologyPune, Maharashtra, IndiaView full profile → , Kirti Hemant Wanjalekirti.wanjale@viit.ac.inDepartment of Computer Engineering Vishwakarma Institute of Information TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Aradhanaa Deshmukharaddhanadeshmukh@mmcoe.edu.inDepartment of Computer Engineering Vishwakarma Institute of Information TechnologyPune, Maharashtra, IndiaView full profile → , Shyamsunder P. Kosbatwarshyamkosbatwar@gmail.comDepartment Computer Science and Engineering Dnyanshree Institute of Engineering and TechnologySatara, Maharashtra, IndiaView full profile → , Anup Ingleanup.ingale@viit.ac.inDepartment of Electronics and Telecommunication Vishwakarma Institute of Information TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Sheela Hundekarisheelahundekari90@gmail.comMIT-ADT UniversitySchool of Computer Applications Pimpri Chinchwad UniversityPune, Maharashtra, 412106, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 01 Apr 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1345
- Pages:
- 341–353
Abstract
In computer music, instrument recognition is a critical part of sound modeling. Pitch, timbre, loudness, duration, and spatialization are all components of musical sounds. All of these components play a significant part in determining the quality of the tonal sound. It is possible to alter the first four parameters, but timbre always poses a challenge [6]. It was inevitable that timbre would take center stage. Musical instruments are distinguished from one other by their distinct sound quality, independent of their pitch or volume. To distinguish between monophonic and polyphonic music recordings, this method might be used. In Musical Information Retrieval, classification plays one of the critical role. Monophonic instrument classification can be found in literature with quiet a substantial combinations of features and classifiers. Polyphonic instrument classification witnessed less references in the literature and is still an area to be explored specifically when it comes to Indian Classical domain. The present paper exactly focusses on this experimentation. Several Indian instruments were used to produce training data sets for the proposed approach’s evaluation purposes. Among the instruments utilized are the flute, harmonium, and sitar. Statistical and spectral factors are used to classify Indian musical instruments along with the Artificial Intelligence-based methods. Hybrid features from multiple domains that extract essential musical properties are extracted. Accuracy is demonstrated through an Indian Musical Instrument SVM and GMM classification. With monophonic sounds, SVM and Polyphonic produce an average accuracy of 89% and 91%. GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33, according to the results of the studies. The future scope of this recognition framework can be an Artificial Intelligence System with a system linked with the Industrial Internet of Things (IIOT) framework to develop a standalone system or application which can be used for real- time classification of instruments.
Keywords
Subject Classifications
68T01 General topics in artificial intelligence68T01 General topics in artificial intelligence
References
[1] J. J. Burred, A. Robel and T. Sikora, “Dynamic Spectral Envelope Modeling for Timbre Analysis of Musical Instrument Sounds”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 3 pp. 663-674 (2010).[2] Sang Hyun Park, “Musical Instrument Extraction through Timbre Classification”, NVIDIA Corporation Santa Clara, CA 95050.[3] Marques and P. J. Moreno, “A Study of Musical Instrument Classification using Gaussian Mixtures Models and Support Vector Machines”, Compaq Cambridge Research Laboratory, vol. Tech. Report 99/4 (1999). [4] Swe Zin Kalayar Khine, Tin Lay New, Haizhou Li, “On Timbre Based Perceptual Feature For Singer Identification”, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore.[5] Giovanni De Poli and Paolo Prandoni, “Sonological Models for Timbre Characterization”, Journal of New Music Research, Vol. 26:2 pp. 170-197 (1997).[6] Christianini, N., and J. Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods”, Cambridge University Press, Cambridge, UK (2000).[7] B. Kostek, “Musical instrument classification and duet analysis employing music information retrieval techniques”, Proceedings of the IEEE, vol. 92, no. 4, pp. 712-729 (2004).[8] Chih-Wei Hsu and Chih-Jen Lin, “A comparison of methods for multiclass support vector machines”, IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425 (2002).[9] Luca Turchet, Carlo Fischione, Georg Essl, DamiaN Kellar,Mathieu Barthet,”Internet of Musical things:Vision and Challenges”, IEE Access,Vol 6, pp. 61994-62017 2018.[10] S. Prabavathy, V. Rathikarani, P. Dhanalakshmi,” Classification of Musical Instruments using SVM and KNN”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online), Volume-9, Issue-7 (May 2020).[11] Lara Haidar-Ahmad,” Music and Instrument Classification using Deep Learning Technics”, CS230: Deep Learning, Winter 2018, Stanford University, CA.[12] S. Prabavathy, V. Rathikarani, P. Dhanalakshmi,” An Enhanced Musical Instrument Classification using Deep Convolutional Neural Network”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4 (November 2019).[13] Utsav Shukla, Utkarsh Tiwari, Vaibhav Chawla , Shailendra Tiwari,” Instrument Classification using image based Transfer Learning”, 2020 5th International Conference on Computing, Communication and Security (ICCCS).[14] S. Prabavathy, V. Rathikarani, P. Dhanalakshmi,” Musical Instruments Classification using Pre-Trained Model”, International Research Journal of Engineering and Technology”, Volume: 07 Issue: 05 (May 2020).[15] Karthikeya Racharla, Vineet Kumar, Chaudhari Bhushan Jayant, Ankit Khairkar, Paturu Harish,” Predominant Musical Instrument Classification based on Spectral Features[16] Anubhav Chhabra; Aryan Veer Singh; Ritesh Srivastava; Veena Mittal,” Drum Instrument Classification Using Machine Learning”, 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)[17] Amit Kumar Gupta, Pushpa Gothwal, Dinesh Goyal & Carlos M. Travieso-Gonzalez (2022) IoT-Galvanized pandemic special E-Toilet for generation of sanitized environment, Journal of Discrete Mathematical Sciences and Cryptography, DOI: 10.1080/09720529.2022.2068607.[18] Amit Kumar Gupta, Vijander Singh, Priya Mathur & Carlos M. Travieso-Gonzalez. Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario, Journal of Interdisciplinary Mathematics, (2020). DOI: https://doi.org/10.1080/09720502.2020.1833458.[19] Jitendra Singh Yadav, Amit Kumar Gupta & Arjit Saxena. A review on gender identification using machine learning based on fingerprints, Journal of Information and Optimization Sciences, 40:5, 1121-1129 (2019), DOI: https://doi.org/10.1080/02522667.2019.1638002.
Views: 290Downloads: 8Citations: 0
