基于神经网络的孟加拉音乐类型分类

Md. Afif Al Mamun, I. Kadir, AKM SHAHARIAR AZAD RABBY, Abdullah Al Azmi
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引用次数: 5

摘要

音乐类型分类是音乐推荐和音乐信息检索的重要内容。使用不同的机器学习方法对英语音乐类型进行分类已经做了很多工作。尽管孟加拉音乐有自己丰富的风格,但目前几乎没有使用机器学习技术对孟加拉音乐的音乐类型进行分类的值得注意的工作。孟加拉音乐有很多类型和风格,可以分为不同的流派。最初,我们正在考虑6种不同的孟加拉音乐流派,如' Bangla Adhunik ', ' Bangla Hip-Hop ', ' Bangla Band music ', ' Nazrulgeeti ', ' Palligeeti ', ' Rabindra Sangeet '等。我们用了250-300首歌。MP3文件)。从数字音频文件(即MP3文件)中提取音频信号的不同时域和频域特征。最后,在本研究中,我们提出了一个深度学习模型(在比较不同模型的表现后)来对孟加拉音乐类型进行多类分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bangla Music Genre Classification Using Neural Network
Music genre classification is very vital for music recommendation and for the retrieval of music information. So many works have already been done for classifying genres of English music using different machine learning approaches. Even though Bangla music is very rich in its own fashion, there is almost no notable work found to classify music genres of Bangla music using machine learning techniques yet. There are so many types and styles of Bangla music which can be classified in different genres. Initially, we're considering 6 different Bangla music genres such as ‘Bangla Adhunik’, ‘Bangla Hip-Hop’, ‘Bangla Band Music’, ‘Nazrulgeeti’, ‘Palligeeti’, ‘Rabindra Sangeet’ etc. We are using 250–300 songs (. MP3 files) for each genre. We extracted different time domain and frequency domain features of audio signals from digital audio files (i.e. MP3 files). Finally, in this study, we proposed a deep learning model (after comparing performances of different models) to do a multiclass classification of Bangla music genres.
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