Music Genre Classification using CCN-based neuralnetworks
محل انتشار: بیست و یکمین کنفرانس سیستم های فازی ایران
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 149
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شناسه ملی سند علمی:
ICFUZZYS21_024
تاریخ نمایه سازی: 16 بهمن 1402
چکیده مقاله:
During the last decade, music streaming services have extended a lot and attracted many users around the world. One of themain challenges in the field of categorizing and recommending music to users is recognizing the genre of music. Music genre is aconventional category used to describe the characteristics of pieces of music that belong to a common tradition. Since genre is a high-levelattribute for a piece of music, it is a great step for correct classification. In this article, a method based on deep learning and ConvolutionalNeural Networks (CNN) is proposed, which performs genre classification with high accuracy. The proposed method consists of three mainphases: data pre-processing, training/verification phase and testing phase. The well-known GTZAN dataset has been used for evaluationof the proposed method, which has ۱۰۰۰ pieces of ۳۰ seconds music in ۱۰ different genres, some of which are: classical, blues, rock, hiphop,etc. Simulation results show that the proposed network can classify the data with an accuracy of ۸۰.۸%, which is better than that ofsome similar previous methods.
کلیدواژه ها:
نویسندگان
Omid Adibfar
M.Sc. student of Artificial Intelligence, Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Seyyed Enayatallah Alavi
Assistant Professor, Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Marjan Naderan
Associate Professor, Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran