[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121853-en":3,"doc-seo-121853-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121853,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","CLASSIFICATION OF KAZAKH MUSIC GENRES USING MACHINE LEARNING TECHNIqUES - Research summary","Article presents an analysis of a Kazakh music dataset containing 800 audio tracks evenly distributed across five genres. The study builds a genre classification pipeline using machine learning models, specifically Decision Tree Classifier and Logistic Regression, preceded by preprocessing and removal of missing or irrelevant data. Dataset patterns are explored through a correlation matrix and data visualization, while PCA reduces dimensionality while preserving variance. Results compare models with F1-score, precision, and recall, using balanced data and cross-validation, and discuss effectiveness for each genre.","DOI: 10. 37943/17NZKG3418  \n© Aigul Mimenbayeva, Gulmira Bekmagambetova, Gulzhan Muratova, Akgul Naizagarayeva, Tleugaisha Ospanova, Assem Konyrkhanova  \n83  \nDOI: 10.37943/17NZKG3418  \nAigul Mimenbayeva  \nMaster of Sciences, Senior Lecturer, Department of Computational and Data Science  \n[aigulka79_79@mail.ru](aigulka79_79@mail.ru), [orcid.org/0000-0003-4652-470X](orcid.org/0000-0003-4652-470X)  \nAstana IT University, Kazakhstan  \nGulmira Bekmagambetova  \nPhD, Senior Lecturer of the Department of Information Systems [gulmirabekmagam@gmail.com](gulmirabekmagam@gmail.com), [orcid.org/0000-0002-8999-793X](orcid.org/0000-0002-8999-793X)S.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nGulzhan Muratova  \nCandidate of Physical and Mathematical Sciences, Acting Associate Professor [mugk1234@gmail.com](mugk1234@gmail.com), [orcid.org/0009-0001-1259-0832](orcid.org/0009-0001-1259-0832)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nAkgul Naizagarayeva  \nMaster of Technical Sciences, Senior Teacher of the Department of Information Systems  \n[akgul_1985@mail.ru](akgul_1985@mail.ru), [orcid.org/0000-0001-6888-1092](orcid.org/0000-0001-6888-1092)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nTleugaisha Ospanova  \nCandidate of Technical Sciences, Acting Professor of the Faculty of Information Technologies  \n[Tleu2009@mail.ru](Tleu2009@mail.ru), [orcid.org/0000-0002-1729-1321](orcid.org/0000-0002-1729-1321)  \nL. N. Gumilyov Eurasian National University, Kazakhstan  \nAssem Konyrkhanova  \nPhD, Acting of Associate Professor of the Faculty of Information Security [ErkeshanK@mail.ru](ErkeshanK@mail.ru), [orcid.org/0000-0002-4901-8901](orcid.org/0000-0002-4901-8901)  \nL. N. Gumilyov Eurasian National University, Kazakhstan  \nCLASSIFICATION OF KAZAKH MUSIC GENRES USING MACHINE LEARNING TECHNIqUES  \nAbstract: This article analysis a Kazakh Music dataset, which consists of 800 audio tracks equally distributed across 5 different genres. The purpose of this research is to classify music genres by using machine learning algorithms Decision Tree Classifier and Logistic regression. Before the classification, the given data was pre-processed, missing or irrelevant data was removed. The given dataset was analyzed using a correlation matrix and data visualization to identify patterns. To reduce the dimension of the original dataset, the PCA method was used while maintaining variance. Several key studies aimed at analyzing and developing machine learning models applied to the classification of musical genres are reviewed.  \nCumulative explained variance was also plotted, which showed the maximum proportion (90%) of discrete values generated from multiple individual samples taken along the Gaussian curve. A comparison of the decision tree model to a logistic regression showed that for f1 Score Logistic regression produced the best result for classical music – 82%, Decision tree classification – 75%. For other genres, the harmonic mean between precision and recall for the logistic regression model is equal to zero, which means that this model completely fails to  \nCopyright © 2024, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license  \n84  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VolUmE 17, mArch 2024  \nclassify the genres Zazz, Kazakh Rock, Kazakh hip hop, Kazakh pop music. Using the Decision tree classifier algorithm, the Zazz and Kazakh pop music genres were not recognized, but Kazakh Rock with an accuracy and completeness of 33%. Overall, the proposed model achievesan accuracy of 60% for the Decision Tree Classifier and 70% for the Logistic regression model on the training and validation sets. For uniform classification, the data were balanced and assessed using the cross-validation method.  \nThe approach used in this study may be useful in classifying different music genres based on audio data without relying on hum","cbCaic2Ml1FXRiRd","https://ap.wps.com/l/cbCaic2Ml1FXRiRd","pdf",1235488,1,12,"English","en",105,"# Introduction\n## Dataset and preprocessing\n## Feature analysis and dimensionality reduction\n## Classification models and evaluation\n## Results and discussion","[{\"question\":\"What dataset and genres are used for Kazakh music classification?\",\"answer\":\"The research uses 800 audio tracks, evenly distributed across five Kazakh music genres. The study treats this labeled set as the basis for model training and evaluation.\"},{\"question\":\"Which machine learning algorithms are applied in the study?\",\"answer\":\"The document applies a Decision Tree Classifier and Logistic Regression to classify music genres from audio-derived features.\"},{\"question\":\"How does the study reduce input dimensionality and improve analysis?\",\"answer\":\"It uses a correlation matrix and data visualization to identify patterns, then applies PCA to reduce the dataset dimensionality while maintaining variance. Balanced evaluation is performed using cross-validation.\"}]","CLASSIFICATION OF KAZAKH MUSIC GENRES USING MACHINE LEARNING TECHNIqUES - Research summary | PDF",1785807245,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"classification-of-kazakh-music-genres-using-machine-learning-techniques-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classification-of-kazakh-music-genres-using-machine-learning-techniques-research-summary/121853/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset and genres are used for Kazakh music classification?","Question",{"text":75,"@type":76},"The research uses 800 audio tracks, evenly distributed across five Kazakh music genres. The study treats this labeled set as the basis for model training and evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are applied in the study?",{"text":80,"@type":76},"The document applies a Decision Tree Classifier and Logistic Regression to classify music genres from audio-derived features.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study reduce input dimensionality and improve analysis?",{"text":84,"@type":76},"It uses a correlation matrix and data visualization to identify patterns, then applies PCA to reduce the dataset dimensionality while maintaining variance. 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