Abstract
This paper proposes a machine learning approach for classifying classical and new Egyptian music by composer and generating new similar music. The proposed system utilizes a convolutional neural network (CNN) for classification and a CNN autoencoder for generation. The dataset used in this project consists of new and classical Egyptian music pieces composed by different composers. To classify the music by composer, each sample is normalized and transformed into a mel spectrogram. The CNN model is trained on the dataset using the mel spectrograms as input features and the composer labels as output classes. The model achieves 81.4\% accuracy in classifying the music by composer, demonstrating the effectiveness of the proposed approach. To generate new music similar to the original pieces, a CNN autoencoder is trained on a similar dataset. The model is trained to encode the mel spectrograms of the original pieces into a lower-dimensional latent space and then decode them back into the original mel spectrogram. The generated music is produced by sampling from the latent space and decoding the samples back into mel spectrograms, which are then transformed into audio. In conclusion, the proposed system provides a promising approach to classifying and generating classical Egyptian music, which can be applied in various musical applications, such as music recommendation systems, music production, and music education.
Abstract (translated)
本文提出了一种利用机器学习方法对经典和现代埃及音乐进行作曲家分类并生成相似新音乐的方法。该系统采用卷积神经网络（CNN）进行分类，并使用CNN自编码器进行生成。该项目所用的数据集包含不同作曲家创作的经典与现代埃及音乐作品。为了按作曲家对音乐进行分类，每个样本都经过归一化处理，并转换为梅尔频谱图。该CNN模型以梅尔频谱图为输入特征，作曲家标签为输出类别，在数据集上进行了训练。实验结果表明，该模型在按作曲家分类音乐时的准确率为81.4%，证明了所提出方法的有效性。为了生成与原作品相似的新音乐，我们还训练了一个类似的CNN自编码器模型。该模型被训练用于将原始作品的梅尔频谱图编码到一个低维潜在空间中，并将其解码回原始梅尔频谱图。新生成的音乐是通过对潜在空间进行采样并将其反向转换为梅尔频谱图，最后再转化为音频文件而获得的。综上所述，所提出的系统为埃及古典音乐的分类和生成提供了一种有前景的方法，该方法可以应用于各种音乐应用中，如音乐推荐系统、音乐制作及音乐教育等场景。
URL
https://arxiv.org/abs/2410.19719
