CLASSIFICATION OF INSTRUMENT SOUNDS WITH IMAGE CLASSIFICATION ALGORITHMS

Year 2023, Volume: 7 Issue: 3, 513 - 519, 31.12.2023

Remzi Gürfidan

https://doi.org/10.46519/ij3dptdi.1330052

Abstract

Classification of audio files using CNN (Convolutional Neural Network) algorithm is an important application in the field of audio processing and artificial intelligence. This process aims to automatically classify audio files into different classes and can be used in speech recognition, emotional analysis, voice-based control systems and many other applications. The aim of this study is to perform spectrum transformation of instrumental sounds and classify them using image classification algorithms. The dataset contains a total of 1500 data from five different instruments. Audio files were processed, and signal and spectrogram images of each audio file were obtained. DenseNet121, ResNet and CNN algorithms were tested in experimental studies. The most successful results belong to the CNN algorithm with 99.34%.

Keywords

Instrument sound classification, machine learning, CNN algorithm

References

• 1. Antti E., ‘Automatic musical instrument recognition’, Master Thesis, TAMPERE UNIVERSITY OF TECHNOLOGY, Finland, 2001.
• 2. Cotton C. V. and Ellis D. P. W., ‘Spectral vs. spectro-temporal features for acoustic event detection’, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Pages. 69–72, 2011,
• 3. Lee H., Largman Y., Pham P., and Ng A. Y., ‘Unsupervised feature learning for audio classification using convolutional deep belief networks’, Adv Neural Inf Process Syst, Vol. 22, 2009.
• 4. Abdel-Hamid O., Mohamed A. R., Jiang H., Deng L., Penn G., and Yu D., ‘Convolutional neural networks for speech recognition’, IEEE Trans Audio Speech Lang Process, Vol. 22, Issue 10, Pages 1533–1545, 2014.
• 5. Özbek, M. E., Savacı, F. A., Genelleştirilmiş Gauss yoğunluk modellemesi ile müzik aletlerinin sınıflandırılması. 2007 IEEE 15th Signal Processing and Communications Applications, 2007.
• 6. Perfecto Herrera-Boyer G. P. S. D., ‘Automatic Classification of Musical Instrument Sounds’, J New Music Res, Vol. 32, Issue 1, Pages 3–21, 2003.
• 7. Aykanat M., Kılıç Ö., Kurt B., and Saryal S., ‘Classification of lung sounds using convolutional neural networks’, EURASIP J Image Video Process, Vol. 2017, Issue 1, Pages 1–9, 2017.
• 8. Acharya J. and Basu A., ‘Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning’, IEEE Trans Biomed Circuits Syst, Vol. 14, Issue 3, Pages 535–544, 2020.
• 9. Redlarski G., Gradolewski D., and Palkowski A., ‘A System for Heart Sounds Classification’, PLoS One, Vol. 9, Issue 11, Pages e112673, 2014.
• 10. Bardou D., Zhang K., and Ahmad S. M., ‘Lung sounds classification using convolutional neural networks’, Artif Intell Med, Vol. 88, Pages 58–69, 2018.
• 11. Barchiesi D., Giannoulis D. D., Stowell D., and Plumbley M. D., ‘Acoustic Scene Classification: Classifying environments from the sounds they produce’, IEEE Signal Process Mag, Vol. 32, Issue 3, Pages 16–34, 2015.
• 12. Demir F., Abdullah D. A., and Sengur A., ‘A New Deep CNN Model for Environmental Sound Classification’, IEEE Access, Vol. 8, Pages 66529–66537, 2020.
• 13. Elbir A. and Aydin N., ‘Music genre classification and music recommendation by using deep learning’, Electron Lett, Vol. 56, Issue 12, Pages 627–629, 2020.
• 14. Oramas S., Barbieri F., Nieto Caballero O., and Serra X., ‘Multimodal deep learning for music genre classification’, Transactions of the International Society for Music Information Retrieval, Vol. 1, Issue 1, Pages 4–21, 2018.
• 15. Zhang J., ‘Music Feature Extraction and Classification Algorithm Based on Deep Learning’, Sci Program, Vol. 2021, 2021,
• 16. Piczak K. J., ‘Environmental sound classification with convolutional neural networks’, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Vol. 2015-November, 2015.
• 17. Xie J., Hu K., Zhu M., Yu J., and Zhu Q., ‘Investigation of Different CNN-Based Models for Improved Bird Sound Classification’, IEEE Access, Vol. 7, Pages 175353–175361, 2019.
• 18. Mekha P., Teeyasuksaet N., Sompowloy T. and Osathanunkul K., "Honey Bee Sound Classification Using Spectrogram Image Features," 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pages 205-209, Chiang Rai, 2022.
• 19. Mushtaq, Z., Su, S. F., & Tran, Q. V. Spectral images based environmental sound classification using CNN with meaningful data augmentation. Applied Acoustics, Vol. 172, Issue 107581, 2021.
• 20. Kim, I., Kim, Y., & Chin, S. (2023). Deep-Learning-Based Sound Classification Model for Concrete Pouring Work Monitoring at a Construction Site. Applied Sciences, Vol 13, Issue 8, Pages 4789.
• 21. Massoudi M., Verma S. and Jain R., "Urban Sound Classification using CNN," 2021 6th International Conference on Inventive Computation Technologies (ICICT), Pages 583-589, Coimbatore, 2021.
• 22. Jaiswal K. and Kalpeshbhai Patel D., "Sound Classification Using Convolutional Neural Networks," 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Pages 81-84, Bangalore, 2018.