| dc.description.abstract | The classification and categorization of Bangla music genres and sentiments play a pivotal role in the development of dedicated online platforms catering to Bengali music enthusiasts. These platforms aim to provide users with a seamless experience, allowing them to explore and discover music that resonates with their tastes and moods. Through effective organization based on genre and sentiment, users can easily navigate vast music libraries, enhancing their overall engagement and satisfaction. While significant strides have been made in the field of music classification and sentiment analysis, there is always room for improvement. Current models have demonstrated promising results, but their performance can be further enhanced through iterative refinement and expansion of the underlying datasets. This continual improvement process is essential for ensuring the accuracy and efficiency of genre classification and sentiment analysis in Bengali music. One approach to improving model performance is the gradual tuning of hyperparameters. Hyperparameters are crucial settings that govern the behavior of machine learning models, and fine-tuning them can have a significant impact on performance. By systematically adjusting these parameters over time, researchers can optimize model performance to achieve better results in genre classification and sentiment analysis tasks. This iterative tuning process allows for the identification of optimal parameter configurations that maximize model accuracy and efficiency. In addition to hyperparameter tuning, expanding the dataset is another key strategy for improving model performance. A larger and more diverse dataset provides the model with a richer source of information, enabling it to learn more effectively and generalize better to unseen data. By incorporating additional songs with accurate features, researchers can enhance the model’s ability to classify music genres and analyze sentiments accurately. This expansion of the dataset not only improves the performance of existing models but also lays the groundwork for the development of more advanced algorithms in the future. The process of dataset expansion involves collecting and annotating a diverse range of Bengali songs, ensuring that they cover various genres and emotional themes. Careful curation of the dataset is essential to maintain quality and relevance, as well as to minimize biases that may affect model performance. Researchers may leverage crowd-sourcing or collaborate with music experts to gather and annotate the data, ensuring its accuracy and comprehensiveness. Once the expanded dataset is in place, researchers can use it to train and evaluate improved models for genre classification and sentiment analysis. By incorporating a larger and more diverse set of examples, these models can learn more robust representations of Bengali music, leading to better performance on real-world tasks. Additionally, researchers can employ advanced techniques such as transfer learning and ensemble methods to further boost model performance and robustness. Overall, the classification and categorization of Bangla music genres and sentiments are essential for the development of effective online platforms that cater to the diverse preferences of Bengali music enthusiasts. Through iterative refinement and expansion of datasets, researchers can continue to improve the accuracy and efficiency of genre classification and sentiment analysis algorithms, ultimately enhancing the user experience and promoting the discovery of new and exciting music. | en_US |
