Texture Feature and Mel-Spectrogram Analysis for Music Sound Classification

The categorization of music has received substantial interest in the management of large-scale databases. However, the sound of music classification (MC) is poorly interesting, making it a big challenge. For this reason, this paper has proposed a new robust combining method based on texture feature with Mel-spectrogram to classify Arabic music sound. A music audio dataset consisting of 404 sound recordings for different four classes of Arabic music sounds has been collected. The collected data became available for free on the Kaggle website. Firstly, music sound is transformed into a Mel spectrogram, and then several texture features are extracted from these Mel spectrogram images. A two-dimensional Haar wavelet is applied to each Mel-spectrogram image, and Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM), and Histogram of Oriented Gradient (HOG) are utilized for feature extraction. K-nearest neighbors (KNN), random forest (RF), decision tree (DT), logistic regression (LR), AdaBoost, extreme gradient boosting (XGB), and support vector machine (SVM) classifiers were utilized in a comparative analysis of Machine Learning (ML) algorithms. Two different datasets have been employed in order to evaluate the effectiveness of our approach: the collected dataset that the authors had gathered and the global GTZAN dataset. Our method demonstrates superior performance with a five-fold cross-validation. The experimental findings indicated that the XGB exhibited a high accuracy with an average performance of 97.80% for accuracy, 97.72% for F1-Score, 97.75% for recall, and 97.81% for precision.

@article{ElAlami2024,
  title     = {Texture Feature and Mel-Spectrogram Analysis for Music Sound Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {M. E. ElAlami and S. M. K. Tobar and S. M. Khater and Eman. A. Esmaeil},
  doi       = {10.14569/IJACSA.2024.0150918},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150918}
}