孟加拉音乐流派分类的深度学习方法

Moumita Sen Sarma, Avishek Das
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引用次数: 1

摘要

音乐类型分类(Music genre classification, MGC)是通过分析音乐信号或歌词,给音乐贴上相应类型标签的过程。随着音乐数据存储的加速增长,MGC可以广泛应用于音乐推荐系统、广告和流媒体服务中,实现系统高效的管理。然而,已经有许多使用不同统计和机器学习方法进行英语音乐分类的工作,但在孟加拉音乐领域没有发现明显的进展。此外,在利用深度学习(DL)方法对不同音乐类型进行分类方面也有一些重要的工作。孟加拉音乐内容丰富,独具特色。此外,在孟加拉音乐领域探索DL方法的程度和范围仍然是潜在的。因此,孟加拉音乐类型分类是深度学习领域中一个相当新的研究领域。在这项工作中,我们构建了一个孟加拉音乐类型分类器(BMGC)来对6种孟加拉音乐类型进行分类:“Adhunik”、“Band”、“Hiphop”、“Nazrulgeeti”、“Lalon”和“Rabindra Sangeet”。我们创建了一个包含2944个孟加拉音乐片段的孟加拉音乐类型分类数据集(以下称为BMGCD),并开发了一个基于门控循环单元的深度学习模型来从音频信号中预测音乐类型。我们开发的模型达到了80.4%和80.6%的f1分数的准确率,超过了相关的现有作品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BMGC: A Deep Learning Approach to Classify Bengali Music Genres
Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: ‘Adhunik’, ‘Band’, ‘Hiphop’, ‘Nazrulgeeti’, ‘Lalon’, and ‘Rabindra Sangeet’. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4% and 80.6% F1-score which surpasses the related existing works.
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