阿拉伯音乐流派识别

Moataz Ahmed, Sherif Fadel, Manal Helal, Abdel Moneim Wahdan
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引用次数: 0

摘要

音乐信息检索(MIR)是一种数据科学应用,对推荐系统、流派识别、指纹识别和新颖性评估等不同任务至关重要。不同的机器学习技术被用于分析数字音乐记录,如聚类、分类、相似性评分和识别不同任务的各种属性。音乐通过各种转换进行数字表示,并成功地对西方音乐进行了聚类和分类。然而,东方音乐是一个挑战,一些技术在土耳其和波斯音乐的聚类和分类方面取得了成功。本研究评估了机器学习算法在预先标记了阿拉伯流派(Maqam)的阿拉伯音乐上的表现。研究引入了阿拉伯音乐数据集的新数据表示,并确定了最合适的机器学习方法和未来的改进措施。
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Arabic Music Genre Identification
Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.
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