| dc.description.abstract | This thesis investigates the task of music genre classification using a deep learning
approach, combining the strength of the Visual Geometry Group Neural Network
(VGGNet) and Long Short Term Memory (LSTM). This study aims to develop an
effective model that can accurately detect genres on complex and large datasets, a
challenging problem in music genre classification. The dataset used in this study consists
of over 10,000 tracks, covering a wide range of genres. To handle the complexity and
diversity of the dataset, the proposed model uses a parallel architecture that can extract
features at different levels of abstraction. The model also comprises a convolutional
branch for extracting high-level spectral features, a recurrent branch for modeling
temporal dependencies in the audio signal, and a hybrid branch that combines all
features. Overall, this study contributes to the field of music genre classification by
providing a robust and accurate model for music genre classification, which can be
applied to various real-world applications, such as music recommendation systems and
content-based music retrieval. | en_US |
