Assessment and Evaluation of Music Genre Classification by employing various AI Techniques

Music genre classification, the task of automatically assigning audio recordings to specific genres, offers exciting possibilities for music recommendation systems, personalized playlists, and music analysis. This abstract explores the potential of (K-NN), a versatile Artificial Intelligence (AI) algorithm, in tackling this challenge- NN excels in its simplicity and interpretability. It classifies new music by identifying its closest neighbors within a labeled dataset based on predefined features, such as tempo, timbre, or spectral content. These neighbors then collectively "vote" on the genre of the new piece. The abstract delves into the feature engineering process, highlighting the crucial role of selecting relevant musical characteristics for accurate classification. Furthermore, the abstract discusses the impact of the "K" hyper parameter, which determines the number of neighbors considered in the voting process. Choosing the optimal "K" value balances accuracy and sensitivity to genre nuances. While K-NN offers advantages in ease of implementation and understanding, the abstract acknowledges its potential drawbacks. The storage requirements can be high for large datasets, and computational costs may increase with data size. It concludes by acknowledging the need for further research to address computational challenges and explore advanced feature engineering techniques to unlock the full potential of K-NN in this exciting domain.