Arabic Music Classification and Generation using Deep Learning

TL;DR

This paper introduces a deep learning system that classifies Egyptian music by composer with 81.4% accuracy and generates new similar music using CNN autoencoders, advancing music classification and synthesis techniques.

Contribution

It presents a novel CNN-based approach for classifying and generating Egyptian music, demonstrating effective use of mel spectrograms and autoencoders for these tasks.

Findings

Achieved 81.4% accuracy in composer classification.

Successfully generated new music samples similar to original pieces.

Demonstrated the effectiveness of CNN autoencoders for music generation.

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.