Document Type : Research Paper

Author

Mosul University - College of Computer Science and Mathematics

10.37652/juaps.2012.63373

Abstract

Artificial neural network is widely used for many computer applications and assure it success in all of these fields, like recognition applications. In this paper the a supervised NN is used to recognize printed musical notes. First music staff image is read, this staff is segmented in to no. of single music note, then feature extraction is performed on results using combination of mathematic and statistic operations. BP now will trained on all extracted feature to recognize the standard printed music notes. This algorithm is applied on many examples and achieve good results and low error rate.

Main Subjects

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  2. C., D.S. and M., E.D.,(1997). An adaptive technique for automated recognition of musical tones. IEEE Comput. Soc. Press, vol.2:1138-1141.
  3. E., A.,(2001). Automatic musical instrument recognition. Tampere University of Technology, Master of Science Thesis, Department of Information Technology., pp(1-3).
  4. E., A., (2003) . Musical instrument recognition using UCA-Based transform of features and discriminatively trained  HMMS. IEEE, vol.2 : 133 – 136.

حساب القیم الذاتیة

تحویل الصورة الى صورة ذات ألوان ثنائیة وتقطیعها

اتخاذ احدى العلامات المثال وتطبیق الخوارزمیة علیها

ازالة الاسطر الزائدة من العلامة

ایجاد مصفوفة التغایر

تدریب شبکة  BP

الشکل(4)خطوات استخلاص خواص السلم الموسیقی

 

F.,S. W. and L.,E.W., (2003). Application of FRM filters for musical notes separation . IEEE computer society press, vol. 1: 731-734.

 

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