Deep Convolutional Neural Network for musical genre classification via new Self Adaptive Sea Lion Optimization

Automatic Music Genre Classification (MGC) is said to be a basic element for retrieving the music information. In fact, music genre labels are very useful to organize albums, songs, and artists in border groups that share similar characteristics. Henceforth, a precise and effective MGC system is required to enhance the retrieved music genres. This paper tactics to propose a new music genre classification model that includes two major processes: Feature extraction and Classification. In the feature extraction phase, features like "non-negative matrix factorization (NMF) features, Short-Time Fourier Transform (STFT) features and pitch features'' are extracted. The extracted features are then subjected to a classification process via Deep Convolutional Neural Network (DCNN) model. In order to improve the classification accuracy, the DCNN model is trained using a new Self Adaptive SA-SLnO (SA-SLnO) model through optimizing the weight. Finally, the performance of adopted work is evaluated over other existing approaches with respect to error analysis and statistical measures, respectively. (C) 2021 Elsevier B.V. All rights reserved.