Classification of music genre using data augmentation in neural network based on Sports universities data

Resumen

The field of artificial intelligence is among the most promising in AI. To train Artificial Intelligence algorithms, a significant quantity of data is required. We employ Sports colleges data augmentation in a neural network based on the real categorization of music genres by utilizing Sound Smith's algorithm. It can categorize music genres with a 60 percent accuracy and employs millions upon millions of audio samples. In numerous real-world applications, the categorization of genres is a crucial undertaking. The need for accurate meta-data for database management and search/storage purposes increases as the amount of music released daily continues to rise, particularly on internet platforms like YouTube music and spottily (a 2016 estimate suggests that tens of millions of songs were released every month on Playlists). The classification of musical genres is based on characteristics taken from a database of football players from sports universities. The classification was accomplished using Deep Learning. Specific musical features are derived from the acoustic waves generated by football players during games. Convolutional Neural Network is utilized for classification (CNN). The classification of musical genres is based on the technology used by football players. The characteristics are taken from the auditory waves produced by football players during games. A comparison is made between the actual categorization procedure and the classification created by CNN. The comparison demonstrates that the conventional approach, which uses classical features, provides a more accurate classification than the CNN-generated classification based on music features.