Scopus Indexed Publications

Paper Details


Title
Bangla Music Genre Classification Using Neural Network
Author
Md. Afif Al Mamun, Abdullah Al Azmi, AKM Shahariar Azad Rabby, Imamul Kadir,
Email
Abstract
Music genre classification is very vital for music recommendation and for the retrieval of music information. So many works have already been done for classifying genres of English music using different machine learning approaches. Even though Bangla music is very rich in its own fashion, there is almost no notable work found to classify music genres of Bangla music using machine learning techniques yet. There are so many types and styles of Bangla music which can be classified in different genres. Initially, we're considering 6 different Bangla music genres such as `Bangla Adhunik', `Bangla Hip-Hop', `Bangla Band Music', `Nazrulgeeti', `Palligeeti', `Rabindra Sangeet' etc. We are using 250-300 songs (. MP3 files) for each genre. We extracted different time domain and frequency domain features of audio signals from digital audio files (i.e. MP3 files). Finally, in this study, we proposed a deep learning model (after comparing performances of different models) to do a multiclass classification of Bangla music genres.

Keywords
Bangla Music Genre Classification , Deep Learning , Zero Crossing Rate , Spectral Centroid , MFC (Mel Frequency Cepstral Coefficient) , Delta , RMSE , Chroma Frequency , Spectral Roll Off , Neural Network
Journal or Conference Name
Proceedings of the 2019 8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019
Publication Year
2020
Indexing
scopus