孟加拉音乐体裁:一种机器分类学习方法

Abhijit Bhowmik, A. E. Chowdhury
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引用次数: 3

摘要

设计自主索引工具以建立富有表现力和高效的方式来描述音乐媒体内容的必要性是公认的。音乐体裁分类系统对音乐数据库的管理和使用具有重要意义。本研究论文提出了一种增强的方法,使用机器学习方法将音乐自动分类为不同的流派,并展示了将所提出的方案应用于大量孟加拉语音乐内容的分类的见解和结果,孟加拉语是一种东南亚语言,具有许多世纪以来发展的各种音乐流派。在音乐特征提取和决策技术的基础上,我们提出了新的特征和程序来提高准确性。我们通过从数百个孟加拉音乐片段的数据集中提取特征并测试自动分类决策来证明所提出方法的有效性。据我们所知,这是第一个专门应用于孟加拉音乐的自动分类技术的发展,而该方法的优越准确性使其普遍适用。
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Genre of Bangla Music: A Machine Classification Learning Approach
The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.
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