The week's pick
Random Articles
Reseach Article
Design of Simulation System based on Digital Processing of Sound to Identify the Genres of Arabic Music
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 26 |
| Year of Publication: 2020 |
| Authors: M.E. ElAlami, S.M.K. Tobar, S.M. Kh, Eman A. Esmaeil |
10.5120/ijca2020920814
|
M.E. ElAlami, S.M.K. Tobar, S.M. Kh, Eman A. Esmaeil . Design of Simulation System based on Digital Processing of Sound to Identify the Genres of Arabic Music. International Journal of Computer Applications. 175, 26 ( Oct 2020), 36-39. DOI=10.5120/ijca2020920814
Abstract
Musical genres identification has attracted considerable attention and interest in many real world applications, and consider an important technology for music information retrieval (MIR). At present, arabic music represents important aspect of arab music heritage in its distinctive and unique nature .This paper presented a system for arabic music analysis, Mel Frequency Cepstral Coefficients (MFCC) and Short Time Energy (STE) are used to extract features for the music signal and (SVM) classifier was technique used for the purpose of classification. Result show that the proposed system is a useful for achieves state-of-the-art performance for arabic musical genres analysis.
References
- George Tzanetakis , et al. Musical genre classification of audio signals. (2002) IEEE Transactions on speech and audio processing 10(5):293–302.
- Hong Yang , et al. Music Genre Classification using Spectral Analysis Techniques With Hybrid Convolution-Recurrent Neural Network. (2019). International Journal of Innovative Technology and Exploring Engineering, 9(1), pp.149-154.
- Nasrullah, Z. and Zhao, Y. (2019). Music Artist Classification with Convolutional Recurrent Neural Networks. 2019 International Joint Conference on Neural Networks (IJCNN).
- Rosner, A. and Kostek, B. (2017). Automatic music genre classification based on musical instrument track separation. Journal of Intelligent Information Systems, 50(2), pp.363-384.
- Liu ,c. and Feng ,L (2019) . Bottom-up Broadcast Neural Network For Music Genre Classification. Elsevier 24 Jan (2019).
- Chillara ,S. and Haldia ,S (2019). Music Genre Classification using Machine Learning Algorithms: A comparison. International Research Journal of Engineering and Technology (IRJET), Volume: 06 Issue: 05 | May 2019.
- Murauer,B .and Specht ,G (2018). Detecting Music Genre Using Extreme Gradient Boosting. Creative Commons Attribution 4.0 International (CC BY 4.0) license.
- Goienetxea, I., Martínez-Otzeta, J., Sierra, B. and Mendialdua, I. (2018). Towards the use of similarity distances to music genre classification: A comparative study. PLOS ONE, 13(2), p.e0191417.
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. pages 1097–1105.
- Bahoura, M. 2009. Pattern recognition methods applied to respiratory sounds classification to normal and wheeze classes. Computers in Biology and Medicine, 39 (9). 824–843.
- Yao, P. and Zhou, K. (2017). Application of short time energy analysis in monitoring the stability of arc sound signal. Measurement, 105, pp.98-105.
- Frihia, H. and Bahi, H. (2017). HMM/SVM segmentation and labelling of Arabic speech for speech recognition applications. International Journal of Speech Technology, 20 (3), pp.563-573.
- Mittal, T. and Sharma, R. (2016). Integrated search technique for parameter determination of SVM for speech recognition. Journal of Central South University, 23(6), pp.1390-1398.
- Londhe, N. and Kshirsagar, G. (2017). Speaker independent isolated words recognition system for Chhattisgarhi dialect. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).
Index Terms
Keywords