Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.193-200
Designing Musical Instrument Identification Systems Using Lightweight Machine Learning Architectures
Jashwanth Krishtamaraj, Devaraj Jagadiswary, Subramani Raja, Shubham Sharma and Alaa Kareem Mohammed Abstract: Musicology is a vast field in which the use of electronic instruments has increased significantly with the development of digital audio workstations and virtual instruments. Selecting the appropriate instrument is crucial for producing high-quality music. The traditional approach to identifying musical instruments relies on manually listening to audio recordings, which is time-consuming and inefficient. Therefore, musical signal processing has become one of the most important tasks in audio signal processing. It enables the identification of instruments in music based on their acoustic features. The proposed work focuses on extracting audio features from WAV files and developing an ensemble model that combines Random Forest and Support Vector Machine classifiers using a voting strategy to identify the instruments used in music. The findings highlight the potential applications of this research in music information retrieval systems and contribute to the growing field of computational musicology by providing a scalable and accurate approach to musical instrument identification, achieving an accuracy of 95%.
Keywords: Support Vector Machine, Time-Frequency Domain, Voting Classifier, Mel-Frequency Cepstral Coefficients, Persian Instruments.
DOI: 10.25673/122113
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References:
- G. Sharma, K. Umapathy, and S. Krishnan, “Trends in audio signal feature extraction methods,” Applied Acoustics, vol. 158, art. no. 107020, 2020.
- M. K. Gourisaria, R. Agrawal, M. Sahni, and P. K. Singh, “Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques,” Discover Internet of Things, vol. 4, no. 1, p. 1, 2024.
- S. M. H. Mousavi, V. B. S. Prasath, and S. M. H. Mousavi, “Persian classical music instrument recognition (PCMIR) using a novel Persian music database,” in Proc. 9th Int. Conf. Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 2019, pp. 122–130, doi: 10.1109/ICCKE48569.2019.8965166.
- Seth814, “Dataset for audio classification,” GitHub repository, 2018. [Online]. Available: https://github.com/seth814/Audio-Classification/tree/master/wavfiles.
- J. H. Foleis and T. F. Tavares, “Texture selection for automatic music genre classification,” Applied Soft Computing, vol. 89, art. no. 106127, 2020.
- D. Kostrzewa, P. Kaminski, and R. Brzeski, “Music genre classification: Looking for the perfect network,” in Proc. Int. Conf. Computational Science, Cham, Switzerland: Springer, June 2021, pp. 55–67.
- S. Chillara, A. S. Kavitha, S. A. Neginhal, S. Haldia, and K. S. Vidyullatha, “Music genre classification using machine learning algorithms: A comparison,” Int. Research Journal of Engineering and Technology, vol. 6, no. 5, pp. 851–858, 2019.
- P. Seetharaman, G. Wichern, S. Venkataramani, and J. Le Roux, “Class-conditional embeddings for music source separation,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 301–305.
- J. Choi, J. Lee, J. Park, and J. Nam, “Zero-shot learning for audio-based music classification and tagging,” arXiv preprint, arXiv:1907.02670, 2019.
- H. Yang and W. Q. Zhang, “Music genre classification using duplicated convolutional layers in neural networks,” in Proc. Interspeech, Sept. 2019, pp. 3382–3386.
- S. Prabavathy, V. Rathikarani, and P. Dhanalakshmi, “Classification of musical instruments using SVM and KNN,” Int. J. Innovative Technology and Exploring Engineering, vol. 9, no. 7, pp. 1186–1190, 2020.
- G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Transactions on Speech and Audio Processing, vol. 10, no. 5, pp. 293–302, 2002.
- G. Agostini, M. Longari, and E. Pollastri, “Musical instrument timbres classification with spectral features,” EURASIP Journal on Advances in Signal Processing, vol. 2003, pp. 1–10, 2003.
- D. H. Rudd, H. Huo, and G. Xu, “Leveraged mel spectrograms using harmonic and percussive components in speech emotion recognition,” in Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, Cham, Switzerland: Springer, May 2022, pp. 392–404.
- S. Kumar and S. Thiruvenkadam, “An analysis of the impact of spectral contrast feature in speech emotion recognition,” Int. J. Recent Contributions in Engineering, Science & IT, vol. 9, no. 2, pp. 87–95, 2021.
- O. O. Abayomi-Alli, R. Damaševičius, A. Qazi, M. Adedoyin-Olowe, and S. Misra, “Data augmentation and deep learning methods in sound classification: A systematic review,” Electronics, vol. 11, no. 22, art. no. 3795, 2022.
- P. Zinemanas, M. Rocamora, M. Miron, F. Font, and X. Serra, “An interpretable deep learning model for automatic sound classification,” Electronics, vol. 10, no. 7, art. no. 850, 2021.
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