@conference {982,
	title = {BMGC: A Deep Learning Approach to Classify Bengali Music Genres},
	booktitle = {Proceedings of the 4th International Conference on Networking, Information Systems \& Security},
	year = {2021},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {<p>Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: {\textquoteleft}Adhunik{\textquoteright}, {\textquoteleft}Band{\textquoteright}, {\textquoteleft}Hiphop{\textquoteright}, {\textquoteleft}Nazrulgeeti{\textquoteright}, {\textquoteleft}Lalon{\textquoteright}, and {\textquoteleft}Rabindra Sangeet{\textquoteright}. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4\% and 80.6\% F1-score which surpasses the related existing works.</p>
},
	keywords = {Bengali music classification, Bengali Music Dataset., Deep Learning, Gated Recurrent Unit (GRU), Mel Frequency Cepstral Coefficient (MFCC), Recurrent Neural Network},
	isbn = {9781450388719},
	doi = {10.1145/3454127.3456593},
	url = {https://doi.org/10.1145/3454127.3456593},
	author = {Sen Sarma, Moumita and Das, Avishek}
}
@article {82,
	title = {Generalized Distance Functions in the Theory of Computation},
	journal = {Computer Journal},
	volume = {53},
	year = {2010},
	pages = {443{\textendash}464},
	abstract = {<p class="rtejustify">We discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.</p>
},
	keywords = {denotational semantics, fixed-point theorems, generalized distance functions, Logic Programming, stable model, supported model, topology, ultra-metrics},
	doi = {10.1093/comjnl/bxm108},
	url = {http://dx.doi.org/10.1093/comjnl/bxm108},
	author = {Anthony K. Seda and Pascal Hitzler}
}
