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/10.1145/3454127.345659300986nas a2200289   4500008004100000245007900041210006900120300000800189653002700197653003300224653003400257653001600291653001600307653002500323653002200348653002000370653003000390653002200420653001200442653001000454100002400464700002400488700002400512700001700536700002400553856011900577       2021                        eng d00aTraditional Bengali Food Classification Using Convolutional Neural Network0 aTraditional Bengali Food Classification Using Convolutional Neur  a1-810aComputational modeling10aConvolutional Neural Network10aConvolutional neural networks10aData models10afine tuning10aimage classification10aImage recognition10aNeural networks10atraditional Bengali foods10aTransfer learning10aTurning10aVGG161 aUddin, Asif, Mahbub1 aMiraj, Abdullah, Al1 aSarma, Moumita, Sen1 aDas, Avishek1 aGani, Md., Manjurul  uhttps://daselab.cs.ksu.edu/publications/traditional-bengali-food-classification-using-convolutional-neural-network