عناصر مشابهة

Arabic Music Analysis Using Artificial Intelligence Techniques

تفصيل البيانات البيبلوغرافية
المصدر:مجلة بحوث التربية النوعية
الناشر: جامعة المنصورة - كلية التربية النوعية
المؤلف الرئيسي: Esmaeil, Eman. A. (مؤلف)
مؤلفين آخرين: Khater, S. M. (Co-Author), Tobar, S. M. K. (Co-Author), El-Alami, M. E. (Co-Author)
المجلد/العدد:ع67
محكمة:نعم
الدولة:مصر
التاريخ الميلادي:2022
الصفحات:1005 - 1021
DOI:10.21608/mbse.2022.257809
ISSN:2314-8683
رقم MD:1334153
نوع المحتوى: بحوث ومقالات
اللغة:English
قواعد المعلومات:EduSearch
مواضيع:
رابط المحتوى:
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024 |3 10.21608/mbse.2022.257809 
041 |a eng 
044 |b مصر 
100 |a Esmaeil, Eman. A.  |e Author  |9 707675 
245 |a Arabic Music Analysis Using Artificial Intelligence Techniques 
260 |b جامعة المنصورة - كلية التربية النوعية  |c 2022  |g مايو 
300 |a 1005 - 1021 
336 |a بحوث ومقالات  |b Article 
520 |b  Singing is the use of the human voice to make musically meaningful sounds, and it is used in most cultures for enjoyment or self-expression. Songs are audio signal and musical instrument representations. Speech, background noise, and music able to be identified by an audio signal analysis and separation system. The singing voice in a song provides useful information on pitch range, music content, music pace, and rhythm. Nowadays, with multimedia technology, there are many audio editing software available as well as audio merging software by mixing singing voice and music together, but most of these applications are in the field of Western music and there is a less application in the field of arabic music as well as its not free. One of the primary qualities that identify music based on a specific set of patterns is genre. However, the genres of Arabic music on the web are loosely defined, making automatic classification of Arabic audio genres difficult. In this paper, a system has been proposed that consider a form of arabic music analysis and classification which is a twostage based system: The first stage is separation between arabic singing voice and melody using CRPCA which is an extension of RPCA in our previous work. Then extracting the arabic musical genres which forms musical melody that had extracted from the previous process. Mel Frequency Cepstral Coefficients and pitch used to extract feature from musical signal, then Supports Vector Machine algorithm used for classification process. The experimental results show that CRPCA can achieve greater separation performance than earlier approaches, particularly when using temporal frequency masking. Furthermore, the duration of operation on CRPCA under the same conditions, is shorter than others, as well as the most important benefit of the separation improvement that its in fact of improving the classification and analysis process in the next stages. 
653 |a الموسيقى العربية  |a استرجاع المعلومات  |a الذكاء الاصطناعي 
692 |b Source Separation  |b Musical Genres Classification  |b Mel Frequency Cepstral Coefficients  |b Music Information Retrieval  |b Pitch Feature  |b Discrete Wavelet Transform  |b Genres of Arabic Music  |b Support Vector Machine  |b Spectrogram 
700 |a Khater, S. M.  |e Co-Author  |9 707676 
700 |a Tobar, S. M. K.  |e Co-Author  |9 707677 
700 |a El-Alami, M. E.  |e Co-Author  |9 707679 
773 |4 التربية والتعليم  |6 Education & Educational Research  |c 021  |e Research Journal of Specific Education  |f Maǧallaẗ Buḥūṯ Al-Tarbiyyaẗ Al-Nawʿiyyaẗ  |l 067  |m ع67  |o 0201  |s مجلة بحوث التربية النوعية  |v 000  |x 2314-8683 
856 |u 0201-000-067-021.pdf  |n https://mbse.journals.ekb.eg/article_257809.html 
930 |d y  |p y  |q n 
995 |a EduSearch 
999 |c 1334153  |d 1334153