02243nas a2200205   4500008004100000020001800041245006800059210006700127260005900194520151500253653003301768653002701801653001801828653003101846653004601877653002901923100002401952700001701976856004401993       2021                        eng d  a978145038871900aBMGC: A Deep Learning Approach to Classify Bengali Music Genres0 aBMGC A Deep Learning Approach to Classify Bengali Music Genres  aNew York, NY, USAbAssociation for Computing Machinery3 a<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: ‘Adhunik’, ‘Band’, ‘Hiphop’, ‘Nazrulgeeti’, ‘Lalon’, and ‘Rabindra Sangeet’. 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>
10aBengali music classification10aBengali Music Dataset.10aDeep Learning10aGated Recurrent Unit (GRU)10aMel Frequency Cepstral Coefficient (MFCC)10aRecurrent Neural Network1 aSarma, Moumita, Sen1 aDas, Avishek  uhttps://doi.org/10.1145/3454127.345659301428nas a2200229   4500008004100000245006400041210006400105300001400169490000700183520074500190653002700935653002500962653003500987653002201022653001701044653002001061653001301081653001801094100002201112700002001134856004401154       2010                        eng d00aGeneralized Distance Functions in the Theory of Computation0 aGeneralized Distance Functions in the Theory of Computation  a443–4640 v533 a<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>
10adenotational semantics10afixed-point theorems10ageneralized distance functions10aLogic Programming10astable model10asupported model10atopology10aultra-metrics1 aSeda, Anthony, K.1 aHitzler, Pascal  uhttp://dx.doi.org/10.1093/comjnl/bxm108